A brief survey of automatic methods for author name disambiguation
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paper_title: Improving Author Coreference by Resource-Bounded Information Gathering from the Web

paper_content:

Accurate entity resolution is sometimes impossible simply due to insufficient information. For example, in research paper author name resolution, even clever use of venue, title and coauthorship relations are often not enough to make a confident coreference decision. This paper presents several methods for increasing accuracy by gathering and integrating additional evidence from the web. We formulate the coreference problem as one of graph partitioning with discriminatively-trained edge weights, and then incorporate web information either as additional features or as additional nodes in the graph. Since the web is too large to incorporate all its data, we need an efficient procedure for selecting a subset of web queries and data. We formally describe the problem of resource bounded information gathering in each of these contexts, and show significant accuracy improvement with low cost.
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paper_title: On co-authorship for author disambiguation

paper_content:

Author name disambiguation deals with clustering the same-name authors into different individuals. To attack the problem, many studies have employed a variety of disambiguation features such as coauthors, titles of papers/publications, topics of articles, emails/affiliations, etc. Among these, co-authorship is the most easily accessible and influential, since inter-person acquaintances represented by co-authorship could discriminate the identities of authors more clearly than other features. This study attempts to explore the net effects of co-authorship on author clustering in bibliographic data. First, to handle the shortage of explicit coauthors listed in known citations, a web-assisted technique of acquiring implicit coauthors of the target author to be disambiguated is proposed. Then, a coauthor disambiguation hypothesis that the identity of an author can be determined by his/her coauthors is examined and confirmed through a variety of author disambiguation experiments.
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paper_title: The open archives initiative: building a low-barrier interoperability framework

paper_content:

The Open Archives Initiative (OAI) develops and promotes interoperabil ity solutions that aim to facilitate the efficient dissemination of content. The roots of the OAI lie in the E-Print community. Over the last year its focus has been extended to include all content providers. This paper describes the recent history of the OAI - its origins in promoting E-Prints, the broadening of its focus, the details of its technical standard for metadata harvesting, the applications of this standard, and future plans.
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paper_title: Improving Grouped-Entity Resolution Using Quasi-Cliques

paper_content:

The entity resolution (ER) problem, which identifies duplicate entities that refer to the same real world entity, is essential in many applications. In this paper, in particular, we focus on resolving entities that contain a group of related elements in them (e.g., an author entity with a list of citations, a singer entity with song list, or an intermediate result by GROUP BY SQL query). Such entities, named as grouped-entities, frequently occur in many applications. The previous approaches toward grouped-entity resolution often rely on textual similarity, and produce a large number of false positives. As a complementing technique, in this paper, we present our experience of applying a recently proposed graph mining technique, Quasi-Clique, atop conventional ER solutions. Our approach exploits contextual information mined from the group of elements per entity in addition to syntactic similarity. Extensive experiments verify that our proposal improves precision and recall up to 83% when used together with a variety of existing ER solutions, but never worsens them.
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paper_title: Author Name Disambiguation for Citations Using Topic and Web Correlation

paper_content:

Today, bibliographic digital libraries play an important role in helping members of academic community search for novel research. In particular, author disambiguation for citations is a major problem during the data integration and cleaning process, since author names are usually very ambiguous. For solving this problem, we proposed two kinds of correlations between citations, namely, Topic Correlationand Web Correlation, to exploit relationships between citations, in order to identify whether two citations with the same author name refer to the same individual.The topic correlation measures the similarity between research topics of two citations; while the Web correlation measures the number of co-occurrence in web pages. We employ a pair-wise grouping algorithm to group citations into clusters. The results of experiments show that the disambiguation accuracy has great improvement when using topic correlation and Web correlation, and Web correlation provides stronger evidences about the authors of citations.
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paper_title: Comparative study of name disambiguation problem using a scalable blocking-based framework

paper_content:

In this paper, we consider the problem of ambiguous author names in bibliographic citations, and comparatively study alternative approaches to identify and correct such name variants (e.g., "Vannevar Bush" and "V. Vush"). Our study is based on a scalable two-step framework, where step 1 is to substantially reduce the number of candidates via blocking, and step 2 is to measure the distance of two names via coauthor information. Combining four blocking methods and seven distance measures on four data sets, we present extensive experimental results, and identify combinations that are scalable and effective to disambiguate author names in citations.
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paper_title: Effective self-training author name disambiguation in scholarly digital libraries

paper_content:

Name ambiguity in the context of bibliographic citation records is a hard problem that affects the quality of services and content in digital libraries and similar systems. Supervised methods that exploit training examples in order to distinguish ambiguous author names are among the most effective solutions for the problem, but they require skilled human annotators in a laborious and continuous process of manually labeling citations in order to provide enough training examples. Thus, addressing the issues of (i) automatic acquisition of examples and (ii) highly effective disambiguation even when only few examples are available, are the need of the hour for such systems. In this paper, we propose a novel two-step disambiguation method, SAND (Self-training Associative Name Disambiguator), that deals with these two issues. The first step eliminates the need of any manual labeling effort by automatically acquiring examples using a clustering method that groups citation records based on the similarity among coauthor names. The second step uses a supervised disambiguation method that is able to detect unseen authors not included in any of the given training examples. Experiments conducted with standard public collections, using the minimum set of attributes present in a citation (i.e., author names, work title and publication venue), demonstrated that our proposed method outperforms representative unsupervised disambiguation methods that exploit similarities between citation records and is as effective as, and in some cases superior to, supervised ones, without manually labeling any training example.
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paper_title: On Graph-Based Name Disambiguation

paper_content:

Name ambiguity stems from the fact that many people or objects share identical names in the real world. Such name ambiguity decreases the performance of document retrieval, Web search, information integration, and may cause confusion in other applications. Due to the same name spellings and lack of information, it is a nontrivial task to distinguish them accurately. In this article, we focus on investigating the problem in digital libraries to distinguish publications written by authors with identical names. We present an effective framework named GHOST (abbreviation for GrapHical framewOrk for name diSambiguaTion), to solve the problem systematically. We devise a novel similarity metric, and utilize only one type of attribute (i.e., coauthorship) in GHOST. Given the similarity matrix, intermediate results are grouped into clusters with a recently introduced powerful clustering algorithm called Affinity Propagation. In addition, as a complementary technique, user feedback can be used to enhance the performance. We evaluated the framework on the real DBLP and PubMed datasets, and the experimental results show that GHOST can achieve both high precision and recall.
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paper_title: A Latent Topic Model for Complete Entity Resolution

paper_content:

In bibliographies like DBLP and Citeseer, there are three kinds of entity-name problems that need to be solved. First, multiple entities share one name, which is called the name sharing problem. Second, one entity has different names, which is called the name variant problem. Third, multiple entities share multiple names, which is called the name mixing problem. We aim to solve these problems based on one model in this paper. We call this task complete entity resolution. Different from previous work, our work use global information based on data with two types of information, words and author names. We propose a generative latent topic model that involves both author names and words — the LDA-dual model, by extending the LDA (Latent Dirichlet Allocation) model. We also propose a method to obtain model parameters that is global information. Based on obtained model parameters, we propose two algorithms to solve the three problems mentioned above. Experimental results demonstrate the effectiveness and great potential of the proposed model and algorithms.
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paper_title: A hierarchical naive Bayes mixture model for name disambiguation in author citations

paper_content:

Because of name variations, an author may have multiple names and multiple authors may share the same name. Such name ambiguity affects the performance of document retrieval, web search, database integration, and may cause improper attribution to authors. This paper presents a hierarchical naive Bayes mixture model, an unsupervised learning approach, for name disambiguation in author citations. This method partitions a collection of citations1 into clusters, with each cluster containing only citations authored by the same author, thus disambiguating authorship in citations to induce author name identities. Three types of citation features are used: co-author names, paper title words, and journal or proceeding title words. The approach is illustrated with 16 name datasets that are constructed based on the publication lists collected from author homepages and DBLP computer science bibliography.
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paper_title: Collective entity resolution in relational data

paper_content:

Many databases contain uncertain and imprecise references to real-world entities. The absence of identifiers for the underlying entities often results in a database which contains multiple references to the same entity. This can lead not only to data redundancy, but also inaccuracies in query processing and knowledge extraction. These problems can be alleviated through the use of entity resolution. Entity resolution involves discovering the underlying entities and mapping each database reference to these entities. Traditionally, entities are resolved using pairwise similarity over the attributes of references. However, there is often additional relational information in the data. Specifically, references to different entities may cooccur. In these cases, collective entity resolution, in which entities for cooccurring references are determined jointly rather than independently, can improve entity resolution accuracy. We propose a novel relational clustering algorithm that uses both attribute and relational information for determining the underlying domain entities, and we give an efficient implementation. We investigate the impact that different relational similarity measures have on entity resolution quality. We evaluate our collective entity resolution algorithm on multiple real-world databases. We show that it improves entity resolution performance over both attribute-based baselines and over algorithms that consider relational information but do not resolve entities collectively. In addition, we perform detailed experiments on synthetically generated data to identify data characteristics that favor collective relational resolution over purely attribute-based algorithms.
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paper_title: Two supervised learning approaches for name disambiguation in author citations

paper_content:

Due to name abbreviations, identical names, name misspellings, and pseudonyms in publications or bibliographies (citations), an author may have multiple names and multiple authors may share the same name. Such name ambiguity affects the performance of document retrieval, Web search, database integration, and may cause improper attribution to authors. We investigate two supervised learning approaches to disambiguate authors in the citations. One approach uses the naive Bayes probability model, a generative model; the other uses support vector machines (SVMs) [V. Vapnik (1995)] and the vector space representation of citations, a discriminative model. Both approaches utilize three types of citation attributes: coauthor names, the title of the paper, and the title of the journal or proceeding. We illustrate these two approaches on two types of data, one collected from the Web, mainly publication lists from homepages, the other collected from the DBLP citation databases.
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paper_title: Disambiguating authors in academic publications using random forests

paper_content:

Users of digital libraries usually want to know the exact author or authors of an article. But different authors may share the same names, either as full names or as initials and last names (complete name change examples are not considered here). In such a case, the user would like the digital library to differentiate among these authors. Name disambiguation can help in many cases; one being a user in a search of all articles written by a particular author. Disambiguation also enables better bibliometric analysis by allowing a more accurate counting and grouping of publications and citations. In this paper, we describe an algorithm for pair-wise disambiguation of author names based on a machine learning classification algorithm, random forests. We define a set of similarity profile features to assist in author disambiguation. Our experiments on the Medline database show that the random forest model outperforms other previously proposed techniques such as those using support-vector machines (SVM). In addition, we demonstrate that the variable importance produced by the random forest model can be used in feature selection with little degradation in the disambiguation accuracy. In particular, the inverse document frequency of author last name and the middle name's similarity alone achieves an accuracy of almost 90%.
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paper_title: Author Disambiguation using Error-driven Machine Learning with a Ranking Loss Function

paper_content:

Author disambiguation is the problem of determining whether records in a publications database refer to the same person. A common supervised machine learning approach is to build a classifier to predict whether a pair of records is coreferent, followed by a clustering step to enforce transitivity. However, this approach ignores powerful evidence obtainable by examining sets (rather than pairs) of records, such as the number of publications or co-authors an author has. In this paper we propose a representation that enables these first-order features over sets of records. We then propose a training algorithm well-suited to this representation that is (1) error-driven in that training examples are generated from incorrect predictions on the training data, and (2) rankbased in that the classifier induces a ranking over candidate predictions. We evaluate our algorithms on three author disambiguation datasets and demonstrate error reductions of up to 60% over the standard binary classification approach. Introduction Record deduplication is the problem of deciding whether two records in a database refer to the same object. This problem is widespread in any large-scale database, and is particularly acute when records are constructed automatically from text mining. Author disambiguation, the problem of deduplicating author records, is a critical concern for digital publication libraries such as Citeseer, DBLP, Rexa, and Google Scholar. Author disambiguation is difficult in these domains because of abbreviations (e.g., Y. Smith) misspellings (e.g., Y. Smiht), and extraction errors (e.g., Smith Statistical). Many supervised machine learning approaches to author disambiguation have been proposed. Most of these are variants of the following recipe: (1) train a binary classifier to predict whether a pair of authors are duplicates, (2) apply the classifier to each pair of ambiguous authors, (3) combine the classification predictions to cluster the records into duplicate sets. Copyright c © 2007, American Association for Artificial Intelligence (www.aaai.org). All rights reserved. This approach can be quite accurate, and is attractive because it builds upon existing machine learning technology (e.g., classification and clustering). However, because the core of this approach is a classifier over record pairs, nowhere are aggregate features of an author modeled explicitly. That is, by restricting the model representation to evidence over pairs of authors, we cannot leverage evidence available from examining more than two records. For example, we would like to model the fact that authors are generally affiliated with only a few institutions, have only one or two different email addresses, and are unlikely to publish more than thirty publications in one year. None of these constraints can be captured with a pairwise classifier, which, for example, can only consider whether pairs of institutions or emails match. In this paper we propose a representation for author disambiguation that enables these aggregate constraints. The representation can be understood as a scoring function over a set of authors, indicating how likely it is that all members of the set are duplicates. While flexible, this new representation can make it difficult to estimate the model parameters from training data. We therefore propose a class of training algorithms to estimate the parameters of models adopting this representation. The method has two main characteristics essential to its performance. First, it is errordriven in that training examples are generated based on mistakes made by the prediction algorithm. This approach focuses training effort on the types of examples expected during prediction. Second, it is rank-based in that the loss function induces a ranking over candidate predictions. By representing the difference between predictions, preferences can be expressed over partially-correct or incomplete solutions; additionally, intractable normalization constants can be avoided because the loss function is a ratio of terms. In the following sections, we describe the representation in more detail, then present our proposed training methods. We evaluate our proposals on three real-world author deduplication datasets and demonstrate errorreductions of up to 60%. Author Title Institution Year Y. Li Understanding Social Networks Stanford 2003 Y. Li Understanding Network Protocols Carnegie Mellon 2002 Y. Li Virtual Network Protocols Peking Univ. 2001 Table 1: Author disambiguation example with multiple institutions. Author Co-authors Title P. Cohen A. Howe How evaluation guides AI research P. Cohen M. Greenberg, A. Howe, ... Trial by Fire: Understanding the design requirements ... in complex environments P. Cohen M. Greenberg MU: a development environment for prospective reasoning systems Table 2: Author disambiguation example with overlapping co-authors.
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paper_title: A Unified Probabilistic Framework for Name Disambiguation in Digital Library

paper_content:

Despite years of research, the name ambiguity problem remains largely unresolved. Outstanding issues include how to capture all information for name disambiguation in a unified approach, and how to determine the number of people K in the disambiguation process. In this paper, we formalize the problem in a unified probabilistic framework, which incorporates both attributes and relationships. Specifically, we define a disambiguation objective function for the problem and propose a two-step parameter estimation algorithm. We also investigate a dynamic approach for estimating the number of people K. Experiments show that our proposed framework significantly outperforms four baseline methods of using clustering algorithms and two other previous methods. Experiments also indicate that the number K automatically found by our method is close to the actual number.
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paper_title: Efficient topic-based unsupervised name disambiguation

paper_content:

Name ambiguity is a special case of identity uncertainty where one person can be referenced by multiple name variations in different situations or even share the same name with other people. In this paper, we focus on the problem of disambiguating person names within web pages and scientific documents. We present an efficient and effective two-stage approach to disambiguate names. In the first stage, two novel topic-based models are proposed by extending two hierarchical Bayesian text models, namely Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA). Our models explicitly introduce a new variable for persons and learn the distribution of topics with regard to persons and words. After learning an initial model, the topic distributions are treated as feature sets and names are disambiguated by leveraging a hierarchical agglomerative clustering method. Experiments on web data and scientific documents from CiteSeer indicate that our approach consistently outperforms other unsupervised learning methods such as spectral clustering and DBSCAN clustering and could be extended to other research fields. We empirically addressed the issue of scalability by disambiguating authors in over 750,000 papers from the entire CiteSeer dataset.
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paper_title: Keeping a digital library clean: new solutions to old problems

paper_content:

Digital Libraries are complex information systems that involve rich sets of digital objects and their respective metadata, along with multiple organizational structures and services (e.g., searching, browsing, and personalization), and are normally built having a target community of users with specific interests. Central to the success of this type of system is the quality of their services and content. In the context of DLs of scientific literature, among the many problems faced to sustain their information quality, two specific ones, related to information consistency, have taken a lot of attention from the research community: name disambiguation and lack of information to access the full-text of cataloged documents. In this paper, we examine these two problems and describe the solutions we have proposed to solve them.
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paper_title: An unsupervised heuristic-based hierarchical method for name disambiguation in bibliographic citations

paper_content:

Name ambiguity in the context of bibliographic citations is a difficult problem which, despite the many efforts from the research community, still has a lot of room for improvement. In this article, we present a heuristic-based hierarchical clustering method to deal with this problem. The method successively fuses clusters of citations of similar author names based on several heuristics and similarity measures on the components of the citations (e.g., coauthor names, work title, and publication venue title). During the disambiguation task, the information about fused clusters is aggregated providing more information for the next round of fusion. In order to demonstrate the effectiveness of our method, we ran a series of experiments in two different collections extracted from real-world digital libraries and compared it, under two metrics, with four representative methods described in the literature. We present comparisons of results using each considered attribute separately (i.e., coauthor names, work title, and publication venue title) with the author name attribute and using all attributes together. These results show that our unsupervised method, when using all attributes, performs competitively against all other methods, under both metrics, loosing only in one case against a supervised method, whose result was very close to ours. Moreover, such results are achieved without the burden of any training and without using any privileged information such as knowing a priori the correct number of clusters. © 2010 Wiley Periodicals, Inc.
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paper_title: A probabilistic similarity metric for medline records: A model for author name disambiguation

paper_content:

We present a model for estimating the probability that a pair of author names (sharing last name and first initial), appearing on two different Medline articles, refer to the same individual. The model uses a simple yet powerful similarity profile between a pair of articles, based on title, journal name, coauthor names, medical subject headings (MeSH), language, affiliation, and name attributes (prevalence in the literature, middle initial, and suffix). The similarity profile distribution is computed from reference sets consisting of pairs of articles containing almost exclusively author matches versus nonmatches, generated in an unbiased manner. Although the match set is generated automatically and might contain a small proportion of nonmatches, the model is quite robust against contamination with nonmatches. We have created a free, public service (“Author-ity”: http://arrowsmith.psych.uic.edu) that takes as input an author’s name given on a specific article, and gives as output a list of all articles with that (last name, first initial) ranked by decreasing similarity, with match probability indicated.
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paper_title: Name disambiguation in author citations using a K-way spectral clustering method

paper_content:

An author may have multiple names and multiple authors may share the same name simply due to name abbreviations, identical names, or name misspellings in publications or bibliographies (citations). This can produce name ambiguity which can affect the performance of document retrieval, web search, and database integration, and may cause improper attribution of credit. Proposed here is an unsupervised learning approach using K-way spectral clustering that disambiguates authors in citations. The approach utilizes three types of citation attributes: co-author names, paper titles, and publication venue titles. The approach is illustrated with 16 name datasets with citations collected from the DBLP database bibliography and author home pages and shows that name disambiguation can be achieved using these citation attributes
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paper_title: Separating the articles of authors with the same name

paper_content:

I describe a method to separate the articles of different authors with the same name. It is based on a distance between any two publications, defined in terms of the probability that they would have as many coincidences if they were drawn at random from all published documents. Articles with a given author name are then clustered according to their distance, so that all articles in a cluster belong very likely to the same author. The method has proven very useful in generating groups of papers that are then selected manually. This simplifies considerably citation analysis when the author publication lists are not available.
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paper_title: A Latent Dirichlet Model for Unsupervised Entity Resolution

paper_content:

Entity resolution has received considerable attention in recent years. Given many references to underlying entities, the goal is to predict which references correspond to the same entity. We show how to extend the Latent Dirichlet Allocation model for this task and propose a probabilistic model for collective entity resolution for relational domains where references are connected to each other. Our approach differs from other recently proposed entity resolution approaches in that it is a) generative, b) does not make pair-wise decisions and c) captures relations between entities through a hidden group variable. We propose a novel sampling algorithm for collective entity resolution which is unsupervised and also takes entity relations into account. Additionally, we do not assume the domain of entities to be known and show how to infer the number of entities from the data. We demonstrate the utility and practicality of our relational entity resolution approach for author resolution in two real-world bibliographic datasets. In addition, we present preliminary results on characterizing conditions under which relational information is useful.
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paper_title: Are your citations clean?

paper_content:

If they are, only one can refer to a distinct document; if not, many can refer to the same document.
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paper_title: Evaluating the Use of Social Networks in Author Name Disambiguation in Digital Libraries

paper_content:

Improvements to lighting systems, especially for entertainment and architectural applications are disclosed, including apparatus that permit a user to readily field-configure power distribution to branch circuits to be dimmed or un-dimmed, single- or multi-phase, as required; communication of data over existing power wiring by variations in output of dimmers; and improved methods for generating, managing, and distributing data specifying the physical and electrical configurations of a lighting system.
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paper_title: Using web information for author name disambiguation

paper_content:

In digital libraries, ambiguous author names may occur due to the existence of multiple authors with the same name (polysemes) or different name variations for the same author (synonyms). We proposed here a new method that uses information available on the Web to deal with both problems at the same time. Our idea consists of gathering information from input citations and submitting queries to a Web search engine, aiming at finding curricula vitae and Web pages containing publications of the ambiguous authors. From the content of documents in the answer sets returned by the Web search engine, useful information that can help in the disambiguation process is extracted. Using this information, author names are disambiguated by leveraging a hierarchical clustering method that groups citations in the same document together in a bottom-up fashion. Experimental results show that the our method yields results that outperform those of two state-of-the-art unsupervised methods and are statistically comparable with those of a supervised one, but requiring no training. We observe gains of up to 65.2% in the pairwise F1 metric when compared with our best unsupervised baseline method.
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paper_title: Improving Author Coreference by Resource-Bounded Information Gathering from the Web

paper_content:

Accurate entity resolution is sometimes impossible simply due to insufficient information. For example, in research paper author name resolution, even clever use of venue, title and coauthorship relations are often not enough to make a confident coreference decision. This paper presents several methods for increasing accuracy by gathering and integrating additional evidence from the web. We formulate the coreference problem as one of graph partitioning with discriminatively-trained edge weights, and then incorporate web information either as additional features or as additional nodes in the graph. Since the web is too large to incorporate all its data, we need an efficient procedure for selecting a subset of web queries and data. We formally describe the problem of resource bounded information gathering in each of these contexts, and show significant accuracy improvement with low cost.
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paper_title: On co-authorship for author disambiguation

paper_content:

Author name disambiguation deals with clustering the same-name authors into different individuals. To attack the problem, many studies have employed a variety of disambiguation features such as coauthors, titles of papers/publications, topics of articles, emails/affiliations, etc. Among these, co-authorship is the most easily accessible and influential, since inter-person acquaintances represented by co-authorship could discriminate the identities of authors more clearly than other features. This study attempts to explore the net effects of co-authorship on author clustering in bibliographic data. First, to handle the shortage of explicit coauthors listed in known citations, a web-assisted technique of acquiring implicit coauthors of the target author to be disambiguated is proposed. Then, a coauthor disambiguation hypothesis that the identity of an author can be determined by his/her coauthors is examined and confirmed through a variety of author disambiguation experiments.
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paper_title: Author name disambiguation in MEDLINE

paper_content:

Background: We recently described “Author-ity,” a model for estimating the probability that two articles in MEDLINE, sharing the same author name, were written by the same individual. Features include shared title words, journal name, coauthors, medical subject headings, language, affiliations, and author name features (middle initial, suffix, and prevalence in MEDLINE). Here we test the hypothesis that the Author-ity model will suffice to disambiguate author names for the vast majority of articles in MEDLINE. Methods: Enhancements include: (a) incorporating first names and their variants, email addresses, and correlations between specific last names and affiliation words; (b) new methods of generating large unbiased training sets; (c) new methods for estimating the prior probability; (d) a weighted least squares algorithm for correcting transitivity violations; and (e) a maximum likelihood based agglomerative algorithm for computing clusters of articles that represent inferred author-individuals. Results: Pairwise comparisons were computed for all author names on all 15.3 million articles in MEDLINE (2006 baseline), that share last name and first initial, to create Author-ity 2006, a database that has each name on each article assigned to one of 6.7 million inferred author-individual clusters. Recall is estimated at ∼98.8p. Lumping (putting two different individuals into the same cluster) affects ∼0.5p of clusters, whereas splitting (assigning articles written by the same individual to >1 cluster) affects ∼2p of articles. Impact: The Author-ity model can be applied generally to other bibliographic databases. Author name disambiguation allows information retrieval and data integration to become person-centered, not just document-centered, setting the stage for new data mining and social network tools that will facilitate the analysis of scholarly publishing and collaboration behavior. Availability: The Author-ity 2006 database is available for nonprofit academic research, and can be freely queried via http://arrowsmith.psych.uic.edu.
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paper_title: Disambiguating Web appearances of people in a social network

paper_content:

Say you are looking for information about a particular person. A search engine returns many pages for that person's name but which pages are about the person you care about, and which are about other people who happen to have the same name? Furthermore, if we are looking for multiple people who are related in some way, how can we best leverage this social network? This paper presents two unsupervised frameworks for solving this problem: one based on link structure of the Web pages, another using Agglomerative/Conglomerative Double Clustering (A/CDC)---an application of a recently introduced multi-way distributional clustering method. To evaluate our methods, we collected and hand-labeled a dataset of over 1000 Web pages retrieved from Google queries on 12 personal names appearing together in someones in an email folder. On this dataset our methods outperform traditional agglomerative clustering by more than 20%, achieving over 80% F-measure.
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paper_title: Improving Grouped-Entity Resolution Using Quasi-Cliques

paper_content:

The entity resolution (ER) problem, which identifies duplicate entities that refer to the same real world entity, is essential in many applications. In this paper, in particular, we focus on resolving entities that contain a group of related elements in them (e.g., an author entity with a list of citations, a singer entity with song list, or an intermediate result by GROUP BY SQL query). Such entities, named as grouped-entities, frequently occur in many applications. The previous approaches toward grouped-entity resolution often rely on textual similarity, and produce a large number of false positives. As a complementing technique, in this paper, we present our experience of applying a recently proposed graph mining technique, Quasi-Clique, atop conventional ER solutions. Our approach exploits contextual information mined from the group of elements per entity in addition to syntactic similarity. Extensive experiments verify that our proposal improves precision and recall up to 83% when used together with a variety of existing ER solutions, but never worsens them.
---
paper_title: Author Name Disambiguation for Citations Using Topic and Web Correlation

paper_content:

Today, bibliographic digital libraries play an important role in helping members of academic community search for novel research. In particular, author disambiguation for citations is a major problem during the data integration and cleaning process, since author names are usually very ambiguous. For solving this problem, we proposed two kinds of correlations between citations, namely, Topic Correlationand Web Correlation, to exploit relationships between citations, in order to identify whether two citations with the same author name refer to the same individual.The topic correlation measures the similarity between research topics of two citations; while the Web correlation measures the number of co-occurrence in web pages. We employ a pair-wise grouping algorithm to group citations into clusters. The results of experiments show that the disambiguation accuracy has great improvement when using topic correlation and Web correlation, and Web correlation provides stronger evidences about the authors of citations.
---
paper_title: Effective self-training author name disambiguation in scholarly digital libraries

paper_content:

Name ambiguity in the context of bibliographic citation records is a hard problem that affects the quality of services and content in digital libraries and similar systems. Supervised methods that exploit training examples in order to distinguish ambiguous author names are among the most effective solutions for the problem, but they require skilled human annotators in a laborious and continuous process of manually labeling citations in order to provide enough training examples. Thus, addressing the issues of (i) automatic acquisition of examples and (ii) highly effective disambiguation even when only few examples are available, are the need of the hour for such systems. In this paper, we propose a novel two-step disambiguation method, SAND (Self-training Associative Name Disambiguator), that deals with these two issues. The first step eliminates the need of any manual labeling effort by automatically acquiring examples using a clustering method that groups citation records based on the similarity among coauthor names. The second step uses a supervised disambiguation method that is able to detect unseen authors not included in any of the given training examples. Experiments conducted with standard public collections, using the minimum set of attributes present in a citation (i.e., author names, work title and publication venue), demonstrated that our proposed method outperforms representative unsupervised disambiguation methods that exploit similarities between citation records and is as effective as, and in some cases superior to, supervised ones, without manually labeling any training example.
---
paper_title: On Graph-Based Name Disambiguation

paper_content:

Name ambiguity stems from the fact that many people or objects share identical names in the real world. Such name ambiguity decreases the performance of document retrieval, Web search, information integration, and may cause confusion in other applications. Due to the same name spellings and lack of information, it is a nontrivial task to distinguish them accurately. In this article, we focus on investigating the problem in digital libraries to distinguish publications written by authors with identical names. We present an effective framework named GHOST (abbreviation for GrapHical framewOrk for name diSambiguaTion), to solve the problem systematically. We devise a novel similarity metric, and utilize only one type of attribute (i.e., coauthorship) in GHOST. Given the similarity matrix, intermediate results are grouped into clusters with a recently introduced powerful clustering algorithm called Affinity Propagation. In addition, as a complementary technique, user feedback can be used to enhance the performance. We evaluated the framework on the real DBLP and PubMed datasets, and the experimental results show that GHOST can achieve both high precision and recall.
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paper_title: Person name disambiguation by bootstrapping

paper_content:

In this paper, we report our system that disambiguates person names in Web search results. The system uses named entities, compound key words, and URLs as features for document similarity calculation, which typically show high precision but low recall clustering results. We propose to use a two-stage clustering algorithm by bootstrapping to improve the low recall values, in which clustering results of the first stage are used to extract features used in the second stage clustering. Experimental results revealed that our algorithm yields better score than the best systems at the latest WePS workshop.
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paper_title: A hierarchical naive Bayes mixture model for name disambiguation in author citations

paper_content:

Because of name variations, an author may have multiple names and multiple authors may share the same name. Such name ambiguity affects the performance of document retrieval, web search, database integration, and may cause improper attribution to authors. This paper presents a hierarchical naive Bayes mixture model, an unsupervised learning approach, for name disambiguation in author citations. This method partitions a collection of citations1 into clusters, with each cluster containing only citations authored by the same author, thus disambiguating authorship in citations to induce author name identities. Three types of citation features are used: co-author names, paper title words, and journal or proceeding title words. The approach is illustrated with 16 name datasets that are constructed based on the publication lists collected from author homepages and DBLP computer science bibliography.
---
paper_title: Collective entity resolution in relational data

paper_content:

Many databases contain uncertain and imprecise references to real-world entities. The absence of identifiers for the underlying entities often results in a database which contains multiple references to the same entity. This can lead not only to data redundancy, but also inaccuracies in query processing and knowledge extraction. These problems can be alleviated through the use of entity resolution. Entity resolution involves discovering the underlying entities and mapping each database reference to these entities. Traditionally, entities are resolved using pairwise similarity over the attributes of references. However, there is often additional relational information in the data. Specifically, references to different entities may cooccur. In these cases, collective entity resolution, in which entities for cooccurring references are determined jointly rather than independently, can improve entity resolution accuracy. We propose a novel relational clustering algorithm that uses both attribute and relational information for determining the underlying domain entities, and we give an efficient implementation. We investigate the impact that different relational similarity measures have on entity resolution quality. We evaluate our collective entity resolution algorithm on multiple real-world databases. We show that it improves entity resolution performance over both attribute-based baselines and over algorithms that consider relational information but do not resolve entities collectively. In addition, we perform detailed experiments on synthetically generated data to identify data characteristics that favor collective relational resolution over purely attribute-based algorithms.
---
paper_title: Two supervised learning approaches for name disambiguation in author citations

paper_content:

Due to name abbreviations, identical names, name misspellings, and pseudonyms in publications or bibliographies (citations), an author may have multiple names and multiple authors may share the same name. Such name ambiguity affects the performance of document retrieval, Web search, database integration, and may cause improper attribution to authors. We investigate two supervised learning approaches to disambiguate authors in the citations. One approach uses the naive Bayes probability model, a generative model; the other uses support vector machines (SVMs) [V. Vapnik (1995)] and the vector space representation of citations, a discriminative model. Both approaches utilize three types of citation attributes: coauthor names, the title of the paper, and the title of the journal or proceeding. We illustrate these two approaches on two types of data, one collected from the Web, mainly publication lists from homepages, the other collected from the DBLP citation databases.
---
paper_title: Scalable name disambiguation using multi-level graph partition

paper_content:

When non-unique values are used as the identifier of entities, due to their homonym, confusion can occur. In particular, when (part of) “names” of entities are used as their identifier, the problem is often referred to as the name disambiguation problem, where goal is to sort out the erroneous entities due to name homonyms (e.g., if only last name is used as the identifier, one cannot distinguish “Vannevar Bush” from “George Bush”). In this paper, in particular, we study the scalability issue of the name disambiguation problem – when (1) a small number of entities with large contents or (2) a large number of entities get un-distinguishable due to homonyms, how to resolve it? We first carefully examine two of the state-of-the-art solutions to the name disambiguation problem, and point out their limitations with respect to scalability. Then, we adapt the multi-level graph partition technique to solve the large-scale name disambiguation problem. Our claim is empirically validated via experimentation – our proposal shows orders of magnitude improvement in terms of performance while maintaining equivalent or reasonable accuracy compared to competing solutions.
---
paper_title: Disambiguating authors in academic publications using random forests

paper_content:

Users of digital libraries usually want to know the exact author or authors of an article. But different authors may share the same names, either as full names or as initials and last names (complete name change examples are not considered here). In such a case, the user would like the digital library to differentiate among these authors. Name disambiguation can help in many cases; one being a user in a search of all articles written by a particular author. Disambiguation also enables better bibliometric analysis by allowing a more accurate counting and grouping of publications and citations. In this paper, we describe an algorithm for pair-wise disambiguation of author names based on a machine learning classification algorithm, random forests. We define a set of similarity profile features to assist in author disambiguation. Our experiments on the Medline database show that the random forest model outperforms other previously proposed techniques such as those using support-vector machines (SVM). In addition, we demonstrate that the variable importance produced by the random forest model can be used in feature selection with little degradation in the disambiguation accuracy. In particular, the inverse document frequency of author last name and the middle name's similarity alone achieves an accuracy of almost 90%.
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paper_title: Name reference resolution in organizational email archives

paper_content:

Online communications provide a rich resource for understanding social networks. Information about the actors, and their dynamic roles and relationships, can be inferred from both the communication content and traffic structure. A key component in the analysis of online communications such as email is the resolution of name references within the body of the message. Name reference resolution relies on the context of the message; both the content of the message and the sender and recipients’ relationships can help to resolve a reference. Here we investigate a variety of approaches which make use of the email traffic network to disambiguate email name references. The email traffic network serves as a proxy for inferring relationships. These relationships in turn help us infer likely candidates for the name references. Our initial findings suggest that simple temporal models can help us effectively resolve name references. For the class of models proposed, performance is maximized by exploiting long-term traffic statistics to rank candidates.
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paper_title: Author Disambiguation using Error-driven Machine Learning with a Ranking Loss Function

paper_content:

Author disambiguation is the problem of determining whether records in a publications database refer to the same person. A common supervised machine learning approach is to build a classifier to predict whether a pair of records is coreferent, followed by a clustering step to enforce transitivity. However, this approach ignores powerful evidence obtainable by examining sets (rather than pairs) of records, such as the number of publications or co-authors an author has. In this paper we propose a representation that enables these first-order features over sets of records. We then propose a training algorithm well-suited to this representation that is (1) error-driven in that training examples are generated from incorrect predictions on the training data, and (2) rankbased in that the classifier induces a ranking over candidate predictions. We evaluate our algorithms on three author disambiguation datasets and demonstrate error reductions of up to 60% over the standard binary classification approach. Introduction Record deduplication is the problem of deciding whether two records in a database refer to the same object. This problem is widespread in any large-scale database, and is particularly acute when records are constructed automatically from text mining. Author disambiguation, the problem of deduplicating author records, is a critical concern for digital publication libraries such as Citeseer, DBLP, Rexa, and Google Scholar. Author disambiguation is difficult in these domains because of abbreviations (e.g., Y. Smith) misspellings (e.g., Y. Smiht), and extraction errors (e.g., Smith Statistical). Many supervised machine learning approaches to author disambiguation have been proposed. Most of these are variants of the following recipe: (1) train a binary classifier to predict whether a pair of authors are duplicates, (2) apply the classifier to each pair of ambiguous authors, (3) combine the classification predictions to cluster the records into duplicate sets. Copyright c © 2007, American Association for Artificial Intelligence (www.aaai.org). All rights reserved. This approach can be quite accurate, and is attractive because it builds upon existing machine learning technology (e.g., classification and clustering). However, because the core of this approach is a classifier over record pairs, nowhere are aggregate features of an author modeled explicitly. That is, by restricting the model representation to evidence over pairs of authors, we cannot leverage evidence available from examining more than two records. For example, we would like to model the fact that authors are generally affiliated with only a few institutions, have only one or two different email addresses, and are unlikely to publish more than thirty publications in one year. None of these constraints can be captured with a pairwise classifier, which, for example, can only consider whether pairs of institutions or emails match. In this paper we propose a representation for author disambiguation that enables these aggregate constraints. The representation can be understood as a scoring function over a set of authors, indicating how likely it is that all members of the set are duplicates. While flexible, this new representation can make it difficult to estimate the model parameters from training data. We therefore propose a class of training algorithms to estimate the parameters of models adopting this representation. The method has two main characteristics essential to its performance. First, it is errordriven in that training examples are generated based on mistakes made by the prediction algorithm. This approach focuses training effort on the types of examples expected during prediction. Second, it is rank-based in that the loss function induces a ranking over candidate predictions. By representing the difference between predictions, preferences can be expressed over partially-correct or incomplete solutions; additionally, intractable normalization constants can be avoided because the loss function is a ratio of terms. In the following sections, we describe the representation in more detail, then present our proposed training methods. We evaluate our proposals on three real-world author deduplication datasets and demonstrate errorreductions of up to 60%. Author Title Institution Year Y. Li Understanding Social Networks Stanford 2003 Y. Li Understanding Network Protocols Carnegie Mellon 2002 Y. Li Virtual Network Protocols Peking Univ. 2001 Table 1: Author disambiguation example with multiple institutions. Author Co-authors Title P. Cohen A. Howe How evaluation guides AI research P. Cohen M. Greenberg, A. Howe, ... Trial by Fire: Understanding the design requirements ... in complex environments P. Cohen M. Greenberg MU: a development environment for prospective reasoning systems Table 2: Author disambiguation example with overlapping co-authors.
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paper_title: A Comparison of String Distance Metrics for Name-Matching Tasks

paper_content:

Using an open-source, Java toolkit of name-matching methods, we experimentally compare string distance metrics on the task of matching entity names. We investigate a number of different metrics proposed by different communities, including edit-distance metrics, fast heuristic string comparators, token-based distance metrics, and hybrid methods. Overall, the best-performing method is a hybrid scheme combining a TFIDF weighting scheme, which is widely used in information retrieval, with the Jaro-Winkler string-distance scheme, which was developed in the probabilistic record linkage community.
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paper_title: A Unified Probabilistic Framework for Name Disambiguation in Digital Library

paper_content:

Despite years of research, the name ambiguity problem remains largely unresolved. Outstanding issues include how to capture all information for name disambiguation in a unified approach, and how to determine the number of people K in the disambiguation process. In this paper, we formalize the problem in a unified probabilistic framework, which incorporates both attributes and relationships. Specifically, we define a disambiguation objective function for the problem and propose a two-step parameter estimation algorithm. We also investigate a dynamic approach for estimating the number of people K. Experiments show that our proposed framework significantly outperforms four baseline methods of using clustering algorithms and two other previous methods. Experiments also indicate that the number K automatically found by our method is close to the actual number.
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paper_title: Efficient topic-based unsupervised name disambiguation

paper_content:

Name ambiguity is a special case of identity uncertainty where one person can be referenced by multiple name variations in different situations or even share the same name with other people. In this paper, we focus on the problem of disambiguating person names within web pages and scientific documents. We present an efficient and effective two-stage approach to disambiguate names. In the first stage, two novel topic-based models are proposed by extending two hierarchical Bayesian text models, namely Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA). Our models explicitly introduce a new variable for persons and learn the distribution of topics with regard to persons and words. After learning an initial model, the topic distributions are treated as feature sets and names are disambiguated by leveraging a hierarchical agglomerative clustering method. Experiments on web data and scientific documents from CiteSeer indicate that our approach consistently outperforms other unsupervised learning methods such as spectral clustering and DBSCAN clustering and could be extended to other research fields. We empirically addressed the issue of scalability by disambiguating authors in over 750,000 papers from the entire CiteSeer dataset.
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paper_title: An unsupervised heuristic-based hierarchical method for name disambiguation in bibliographic citations

paper_content:

Name ambiguity in the context of bibliographic citations is a difficult problem which, despite the many efforts from the research community, still has a lot of room for improvement. In this article, we present a heuristic-based hierarchical clustering method to deal with this problem. The method successively fuses clusters of citations of similar author names based on several heuristics and similarity measures on the components of the citations (e.g., coauthor names, work title, and publication venue title). During the disambiguation task, the information about fused clusters is aggregated providing more information for the next round of fusion. In order to demonstrate the effectiveness of our method, we ran a series of experiments in two different collections extracted from real-world digital libraries and compared it, under two metrics, with four representative methods described in the literature. We present comparisons of results using each considered attribute separately (i.e., coauthor names, work title, and publication venue title) with the author name attribute and using all attributes together. These results show that our unsupervised method, when using all attributes, performs competitively against all other methods, under both metrics, loosing only in one case against a supervised method, whose result was very close to ours. Moreover, such results are achieved without the burden of any training and without using any privileged information such as knowing a priori the correct number of clusters. © 2010 Wiley Periodicals, Inc.
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paper_title: A probabilistic similarity metric for medline records: A model for author name disambiguation

paper_content:

We present a model for estimating the probability that a pair of author names (sharing last name and first initial), appearing on two different Medline articles, refer to the same individual. The model uses a simple yet powerful similarity profile between a pair of articles, based on title, journal name, coauthor names, medical subject headings (MeSH), language, affiliation, and name attributes (prevalence in the literature, middle initial, and suffix). The similarity profile distribution is computed from reference sets consisting of pairs of articles containing almost exclusively author matches versus nonmatches, generated in an unbiased manner. Although the match set is generated automatically and might contain a small proportion of nonmatches, the model is quite robust against contamination with nonmatches. We have created a free, public service (“Author-ity”: http://arrowsmith.psych.uic.edu) that takes as input an author’s name given on a specific article, and gives as output a list of all articles with that (last name, first initial) ranked by decreasing similarity, with match probability indicated.
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paper_title: Name disambiguation in author citations using a K-way spectral clustering method

paper_content:

An author may have multiple names and multiple authors may share the same name simply due to name abbreviations, identical names, or name misspellings in publications or bibliographies (citations). This can produce name ambiguity which can affect the performance of document retrieval, web search, and database integration, and may cause improper attribution of credit. Proposed here is an unsupervised learning approach using K-way spectral clustering that disambiguates authors in citations. The approach utilizes three types of citation attributes: co-author names, paper titles, and publication venue titles. The approach is illustrated with 16 name datasets with citations collected from the DBLP database bibliography and author home pages and shows that name disambiguation can be achieved using these citation attributes
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paper_title: Separating the articles of authors with the same name

paper_content:

I describe a method to separate the articles of different authors with the same name. It is based on a distance between any two publications, defined in terms of the probability that they would have as many coincidences if they were drawn at random from all published documents. Articles with a given author name are then clustered according to their distance, so that all articles in a cluster belong very likely to the same author. The method has proven very useful in generating groups of papers that are then selected manually. This simplifies considerably citation analysis when the author publication lists are not available.
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paper_title: A Latent Dirichlet Model for Unsupervised Entity Resolution

paper_content:

Entity resolution has received considerable attention in recent years. Given many references to underlying entities, the goal is to predict which references correspond to the same entity. We show how to extend the Latent Dirichlet Allocation model for this task and propose a probabilistic model for collective entity resolution for relational domains where references are connected to each other. Our approach differs from other recently proposed entity resolution approaches in that it is a) generative, b) does not make pair-wise decisions and c) captures relations between entities through a hidden group variable. We propose a novel sampling algorithm for collective entity resolution which is unsupervised and also takes entity relations into account. Additionally, we do not assume the domain of entities to be known and show how to infer the number of entities from the data. We demonstrate the utility and practicality of our relational entity resolution approach for author resolution in two real-world bibliographic datasets. In addition, we present preliminary results on characterizing conditions under which relational information is useful.
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paper_title: When A. Rose is not A. Rose: the vagaries of author searching.

paper_content:

Creation of a faculty database is a challenging but worthwhile project. In 2002, librarians at the University of Missouri-Columbia were approached by the School of Medicine to create a database of faculty publications. This article outlines the project and gives special emphasis to the problems of author searching. Discussion items include author searching syntax and providing ideas for how to determine if a publication was written by a particular author.
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paper_title: Using a knowledge base to disambiguate personal name in web search results

paper_content:

Results of queries by personal names often contain documents related to several people because of the namesake problem. In order to differentiate documents related to different people, an effective method is needed to measure document similarities and to find documents related to the same person. Some previous researchers have used the vector space model or have tried to extract common named entities for measuring similarities. We propose a new method that uses Web directories as a knowledge base to find shared contexts in document pairs and uses the measurement of shared contexts to determine similarities between document pairs. Experimental results show that our proposed method outperforms the vector space model method and the named entity recognition method.
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paper_title: Using web information for author name disambiguation

paper_content:

In digital libraries, ambiguous author names may occur due to the existence of multiple authors with the same name (polysemes) or different name variations for the same author (synonyms). We proposed here a new method that uses information available on the Web to deal with both problems at the same time. Our idea consists of gathering information from input citations and submitting queries to a Web search engine, aiming at finding curricula vitae and Web pages containing publications of the ambiguous authors. From the content of documents in the answer sets returned by the Web search engine, useful information that can help in the disambiguation process is extracted. Using this information, author names are disambiguated by leveraging a hierarchical clustering method that groups citations in the same document together in a bottom-up fashion. Experimental results show that the our method yields results that outperform those of two state-of-the-art unsupervised methods and are statistically comparable with those of a supervised one, but requiring no training. We observe gains of up to 65.2% in the pairwise F1 metric when compared with our best unsupervised baseline method.
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paper_title: Author name disambiguation in MEDLINE

paper_content:

Background: We recently described “Author-ity,” a model for estimating the probability that two articles in MEDLINE, sharing the same author name, were written by the same individual. Features include shared title words, journal name, coauthors, medical subject headings, language, affiliations, and author name features (middle initial, suffix, and prevalence in MEDLINE). Here we test the hypothesis that the Author-ity model will suffice to disambiguate author names for the vast majority of articles in MEDLINE. Methods: Enhancements include: (a) incorporating first names and their variants, email addresses, and correlations between specific last names and affiliation words; (b) new methods of generating large unbiased training sets; (c) new methods for estimating the prior probability; (d) a weighted least squares algorithm for correcting transitivity violations; and (e) a maximum likelihood based agglomerative algorithm for computing clusters of articles that represent inferred author-individuals. Results: Pairwise comparisons were computed for all author names on all 15.3 million articles in MEDLINE (2006 baseline), that share last name and first initial, to create Author-ity 2006, a database that has each name on each article assigned to one of 6.7 million inferred author-individual clusters. Recall is estimated at ∼98.8p. Lumping (putting two different individuals into the same cluster) affects ∼0.5p of clusters, whereas splitting (assigning articles written by the same individual to >1 cluster) affects ∼2p of articles. Impact: The Author-ity model can be applied generally to other bibliographic databases. Author name disambiguation allows information retrieval and data integration to become person-centered, not just document-centered, setting the stage for new data mining and social network tools that will facilitate the analysis of scholarly publishing and collaboration behavior. Availability: The Author-ity 2006 database is available for nonprofit academic research, and can be freely queried via http://arrowsmith.psych.uic.edu.
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paper_title: Incremental Unsupervised Name Disambiguation in Cleaned Digital Libraries

paper_content:

Name ambiguity in the context of bibliographic citations is one of the hardest problems currently faced by the Digital Library (DL) community. Here we deal with the problem of disambiguating new citations records inserted into a cleaned DL, without the need to process the whole collection, which is usually necessary for unsupervised methods. Although supervised solutions can deal with this situation, there is the costly burden of generating training data besides the fact that these methods cannot handle well the insertion of records of new authors not already existent in the repository. In this article, we propose a new unsupervised method that identifies the correct authors of the new citation records to be inserted in a DL. The method is based on heuristics that are also used to identify whether the new records belong to authors already in the digital library or not, correctly identifying new authors in most cases. Our experimental evaluation, using synthetic and real datasets, shows gains of up to 19\% when compared to a state-of-the-art method without the cost of having to disambiguate the whole DL at each new load (as done by unsupervised methods) or the need for any training (as done by supervised methods).
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paper_title: Spectral Relaxation for K means Clustering

paper_content:

The popular K-means clustering partitions a data set by minimizing a sum-of-squares cost function. A coordinate descend method is then used to find local minima. In this paper we show that the minimization can be reformulated as a trace maximization problem associated with the Gram matrix of the data vectors. Furthermore, we show that a relaxed version of the trace maximization problem possesses global optimal solutions which can be obtained by computing a partial eigendecomposition of the Gram matrix, and the cluster assignment for each data vectors can be found by computing a pivoted QR decomposition of the eigenvector matrix. As a by-product we also derive a lower bound for the minimum of the sum-of-squares cost function.
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paper_title: Non-metric affinity propagation for unsupervised image categorization

paper_content:

Unsupervised categorization of images or image parts is often needed for image and video summarization or as a preprocessing step in supervised methods for classification, tracking and segmentation. While many metric-based techniques have been applied to this problem in the vision community, often, the most natural measures of similarity (e.g., number of matching SIFT features) between pairs of images or image parts is non-metric. Unsupervised categorization by identifying a subset of representative exemplars can be efficiently performed with the recently-proposed 'affinity propagation' algorithm. In contrast to k-centers clustering, which iteratively refines an initial randomly-chosen set of exemplars, affinity propagation simultaneously considers all data points as potential exemplars and iteratively exchanges messages between data points until a good solution emerges. When applied to the Olivetti face data set using a translation-invariant non-metric similarity, affinity propagation achieves a much lower reconstruction error and nearly halves the classification error rate, compared to state-of-the-art techniques. For the more challenging problem of unsupervised categorization of images from the CaltechlOl data set, we derived non-metric similarities between pairs of images by matching SIFT features. Affinity propagation successfully identifies meaningful categories, which provide a natural summarization of the training images and can be used to classify new input images.
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paper_title: Maximum likelihood from incomplete data via the EM algorithm

paper_content:

Vibratory power unit for vibrating conveyers and screens comprising an asynchronous polyphase motor, at least one pair of associated unbalanced masses disposed on the shaft of said motor, with the first mass of a pair of said unbalanced masses being rigidly fastened to said shaft and with said second mass of said pair being movably arranged relative to said first mass, means for controlling and regulating the conveying rate during conveyer operation by varying the rotational speed of said motor between predetermined minimum and maximum values, said second mass being movably outwardly by centrifugal force against the pressure of spring means, said spring means being prestressed in such a manner that said second mass is, at rotational motor speeds lower than said minimum speed, held in its initial position, and at motor speeds between said lower and upper values in positions which are radially offset with respect to the axis of said motor to an extent depending on the value of said rotational motor speed.
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paper_title: Improving Grouped-Entity Resolution Using Quasi-Cliques

paper_content:

The entity resolution (ER) problem, which identifies duplicate entities that refer to the same real world entity, is essential in many applications. In this paper, in particular, we focus on resolving entities that contain a group of related elements in them (e.g., an author entity with a list of citations, a singer entity with song list, or an intermediate result by GROUP BY SQL query). Such entities, named as grouped-entities, frequently occur in many applications. The previous approaches toward grouped-entity resolution often rely on textual similarity, and produce a large number of false positives. As a complementing technique, in this paper, we present our experience of applying a recently proposed graph mining technique, Quasi-Clique, atop conventional ER solutions. Our approach exploits contextual information mined from the group of elements per entity in addition to syntactic similarity. Extensive experiments verify that our proposal improves precision and recall up to 83% when used together with a variety of existing ER solutions, but never worsens them.
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paper_title: On Graph-Based Name Disambiguation

paper_content:

Name ambiguity stems from the fact that many people or objects share identical names in the real world. Such name ambiguity decreases the performance of document retrieval, Web search, information integration, and may cause confusion in other applications. Due to the same name spellings and lack of information, it is a nontrivial task to distinguish them accurately. In this article, we focus on investigating the problem in digital libraries to distinguish publications written by authors with identical names. We present an effective framework named GHOST (abbreviation for GrapHical framewOrk for name diSambiguaTion), to solve the problem systematically. We devise a novel similarity metric, and utilize only one type of attribute (i.e., coauthorship) in GHOST. Given the similarity matrix, intermediate results are grouped into clusters with a recently introduced powerful clustering algorithm called Affinity Propagation. In addition, as a complementary technique, user feedback can be used to enhance the performance. We evaluated the framework on the real DBLP and PubMed datasets, and the experimental results show that GHOST can achieve both high precision and recall.
---
paper_title: Collective entity resolution in relational data

paper_content:

Many databases contain uncertain and imprecise references to real-world entities. The absence of identifiers for the underlying entities often results in a database which contains multiple references to the same entity. This can lead not only to data redundancy, but also inaccuracies in query processing and knowledge extraction. These problems can be alleviated through the use of entity resolution. Entity resolution involves discovering the underlying entities and mapping each database reference to these entities. Traditionally, entities are resolved using pairwise similarity over the attributes of references. However, there is often additional relational information in the data. Specifically, references to different entities may cooccur. In these cases, collective entity resolution, in which entities for cooccurring references are determined jointly rather than independently, can improve entity resolution accuracy. We propose a novel relational clustering algorithm that uses both attribute and relational information for determining the underlying domain entities, and we give an efficient implementation. We investigate the impact that different relational similarity measures have on entity resolution quality. We evaluate our collective entity resolution algorithm on multiple real-world databases. We show that it improves entity resolution performance over both attribute-based baselines and over algorithms that consider relational information but do not resolve entities collectively. In addition, we perform detailed experiments on synthetically generated data to identify data characteristics that favor collective relational resolution over purely attribute-based algorithms.
---
paper_title: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise

paper_content:

Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, we present the new clustering algorithm DBSCAN relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN requires only one input parameter and supports the user in determining an appropriate value for it. We performed an experimental evaluation of the effectiveness and efficiency of DBSCAN using synthetic data and real data of the SEQUOIA 2000 benchmark. The results of our experiments demonstrate that (1) DBSCAN is significantly more effective in discovering clusters of arbitrary shape than the well-known algorithm CLAR-ANS, and that (2) DBSCAN outperforms CLARANS by a factor of more than 100 in terms of efficiency.
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paper_title: Large Margin Classification Using the Perceptron Algorithm

paper_content:

We introduce and analyze a new algorithm for linear classification which combines Rosenblatt's perceptron algorithm with Helmbold and Warmuth's leave-one-out method. Like Vapnik's maximal-margin classifier, our algorithm takes advantage of data that are linearly separable with large margins. Compared to Vapnik's algorithm, however, ours is much simpler to implement, and much more efficient in terms of computation time. We also show that our algorithm can be efficiently used in very high dimensional spaces using kernel functions. We performed some experiments using our algorithm, and some variants of it, for classifying images of handwritten digits. The performance of our algorithm is close to, but not as good as, the performance of maximal-margin classifiers on the same problem, while saving significantly on computation time and programming effort.
---
paper_title: Scalable name disambiguation using multi-level graph partition

paper_content:

When non-unique values are used as the identifier of entities, due to their homonym, confusion can occur. In particular, when (part of) “names” of entities are used as their identifier, the problem is often referred to as the name disambiguation problem, where goal is to sort out the erroneous entities due to name homonyms (e.g., if only last name is used as the identifier, one cannot distinguish “Vannevar Bush” from “George Bush”). In this paper, in particular, we study the scalability issue of the name disambiguation problem – when (1) a small number of entities with large contents or (2) a large number of entities get un-distinguishable due to homonyms, how to resolve it? We first carefully examine two of the state-of-the-art solutions to the name disambiguation problem, and point out their limitations with respect to scalability. Then, we adapt the multi-level graph partition technique to solve the large-scale name disambiguation problem. Our claim is empirically validated via experimentation – our proposal shows orders of magnitude improvement in terms of performance while maintaining equivalent or reasonable accuracy compared to competing solutions.
---
paper_title: Disambiguating authors in academic publications using random forests

paper_content:

Users of digital libraries usually want to know the exact author or authors of an article. But different authors may share the same names, either as full names or as initials and last names (complete name change examples are not considered here). In such a case, the user would like the digital library to differentiate among these authors. Name disambiguation can help in many cases; one being a user in a search of all articles written by a particular author. Disambiguation also enables better bibliometric analysis by allowing a more accurate counting and grouping of publications and citations. In this paper, we describe an algorithm for pair-wise disambiguation of author names based on a machine learning classification algorithm, random forests. We define a set of similarity profile features to assist in author disambiguation. Our experiments on the Medline database show that the random forest model outperforms other previously proposed techniques such as those using support-vector machines (SVM). In addition, we demonstrate that the variable importance produced by the random forest model can be used in feature selection with little degradation in the disambiguation accuracy. In particular, the inverse document frequency of author last name and the middle name's similarity alone achieves an accuracy of almost 90%.
---
paper_title: Ultraconservative online algorithms for multiclass problems

paper_content:

In this paper we study a paradigm to generalize online classification algorithms for binary classification problems to multiclass problems. The particular hypotheses we investigate maintain one prototype vector per class. Given an input instance, a multiclass hypothesis computes a similarity-score between each prototype and the input instance and sets the predicted label to be the index of the prototype achieving the highest similarity. To design and analyze the learning algorithms in this paper we introduce the notion of ultraconservativeness. Ultraconservative algorithms are algorithms that update only the prototypes attaining similarity-scores which are higher than the score of the correct label's prototype. We start by describing a family of additive ultraconservative algorithms where each algorithm in the family updates its prototypes by finding a feasible solution for a set of linear constraints that depend on the instantaneous similarity-scores. We then discuss a specific online algorithm that seeks a set of prototypes which have a small norm. The resulting algorithm, which we term MIRA (for Margin Infused Relaxed Algorithm) is ultraconservative as well. We derive mistake bounds for all the algorithms and provide further analysis of MIRA using a generalized notion of the margin for multiclass problems. We discuss the form the algorithms take in the binary case and show that all the algorithms from the first family reduce to the Perceptron algorithm while MIRA provides a new Perceptron-like algorithm with a margin-dependent learning rate. We then return to multiclass problems and describe an analogous multiplicative family of algorithms with corresponding mistake bounds. We end the formal part by deriving and analyzing a multiclass version of Li and Long's ROMMA algorithm. We conclude with a discussion of experimental results that demonstrate the merits of our algorithms.
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paper_title: Name reference resolution in organizational email archives

paper_content:

Online communications provide a rich resource for understanding social networks. Information about the actors, and their dynamic roles and relationships, can be inferred from both the communication content and traffic structure. A key component in the analysis of online communications such as email is the resolution of name references within the body of the message. Name reference resolution relies on the context of the message; both the content of the message and the sender and recipients’ relationships can help to resolve a reference. Here we investigate a variety of approaches which make use of the email traffic network to disambiguate email name references. The email traffic network serves as a proxy for inferring relationships. These relationships in turn help us infer likely candidates for the name references. Our initial findings suggest that simple temporal models can help us effectively resolve name references. For the class of models proposed, performance is maximized by exploiting long-term traffic statistics to rank candidates.
---
paper_title: Author Disambiguation using Error-driven Machine Learning with a Ranking Loss Function

paper_content:

Author disambiguation is the problem of determining whether records in a publications database refer to the same person. A common supervised machine learning approach is to build a classifier to predict whether a pair of records is coreferent, followed by a clustering step to enforce transitivity. However, this approach ignores powerful evidence obtainable by examining sets (rather than pairs) of records, such as the number of publications or co-authors an author has. In this paper we propose a representation that enables these first-order features over sets of records. We then propose a training algorithm well-suited to this representation that is (1) error-driven in that training examples are generated from incorrect predictions on the training data, and (2) rankbased in that the classifier induces a ranking over candidate predictions. We evaluate our algorithms on three author disambiguation datasets and demonstrate error reductions of up to 60% over the standard binary classification approach. Introduction Record deduplication is the problem of deciding whether two records in a database refer to the same object. This problem is widespread in any large-scale database, and is particularly acute when records are constructed automatically from text mining. Author disambiguation, the problem of deduplicating author records, is a critical concern for digital publication libraries such as Citeseer, DBLP, Rexa, and Google Scholar. Author disambiguation is difficult in these domains because of abbreviations (e.g., Y. Smith) misspellings (e.g., Y. Smiht), and extraction errors (e.g., Smith Statistical). Many supervised machine learning approaches to author disambiguation have been proposed. Most of these are variants of the following recipe: (1) train a binary classifier to predict whether a pair of authors are duplicates, (2) apply the classifier to each pair of ambiguous authors, (3) combine the classification predictions to cluster the records into duplicate sets. Copyright c © 2007, American Association for Artificial Intelligence (www.aaai.org). All rights reserved. This approach can be quite accurate, and is attractive because it builds upon existing machine learning technology (e.g., classification and clustering). However, because the core of this approach is a classifier over record pairs, nowhere are aggregate features of an author modeled explicitly. That is, by restricting the model representation to evidence over pairs of authors, we cannot leverage evidence available from examining more than two records. For example, we would like to model the fact that authors are generally affiliated with only a few institutions, have only one or two different email addresses, and are unlikely to publish more than thirty publications in one year. None of these constraints can be captured with a pairwise classifier, which, for example, can only consider whether pairs of institutions or emails match. In this paper we propose a representation for author disambiguation that enables these aggregate constraints. The representation can be understood as a scoring function over a set of authors, indicating how likely it is that all members of the set are duplicates. While flexible, this new representation can make it difficult to estimate the model parameters from training data. We therefore propose a class of training algorithms to estimate the parameters of models adopting this representation. The method has two main characteristics essential to its performance. First, it is errordriven in that training examples are generated based on mistakes made by the prediction algorithm. This approach focuses training effort on the types of examples expected during prediction. Second, it is rank-based in that the loss function induces a ranking over candidate predictions. By representing the difference between predictions, preferences can be expressed over partially-correct or incomplete solutions; additionally, intractable normalization constants can be avoided because the loss function is a ratio of terms. In the following sections, we describe the representation in more detail, then present our proposed training methods. We evaluate our proposals on three real-world author deduplication datasets and demonstrate errorreductions of up to 60%. Author Title Institution Year Y. Li Understanding Social Networks Stanford 2003 Y. Li Understanding Network Protocols Carnegie Mellon 2002 Y. Li Virtual Network Protocols Peking Univ. 2001 Table 1: Author disambiguation example with multiple institutions. Author Co-authors Title P. Cohen A. Howe How evaluation guides AI research P. Cohen M. Greenberg, A. Howe, ... Trial by Fire: Understanding the design requirements ... in complex environments P. Cohen M. Greenberg MU: a development environment for prospective reasoning systems Table 2: Author disambiguation example with overlapping co-authors.
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paper_title: An unsupervised heuristic-based hierarchical method for name disambiguation in bibliographic citations

paper_content:

Name ambiguity in the context of bibliographic citations is a difficult problem which, despite the many efforts from the research community, still has a lot of room for improvement. In this article, we present a heuristic-based hierarchical clustering method to deal with this problem. The method successively fuses clusters of citations of similar author names based on several heuristics and similarity measures on the components of the citations (e.g., coauthor names, work title, and publication venue title). During the disambiguation task, the information about fused clusters is aggregated providing more information for the next round of fusion. In order to demonstrate the effectiveness of our method, we ran a series of experiments in two different collections extracted from real-world digital libraries and compared it, under two metrics, with four representative methods described in the literature. We present comparisons of results using each considered attribute separately (i.e., coauthor names, work title, and publication venue title) with the author name attribute and using all attributes together. These results show that our unsupervised method, when using all attributes, performs competitively against all other methods, under both metrics, loosing only in one case against a supervised method, whose result was very close to ours. Moreover, such results are achieved without the burden of any training and without using any privileged information such as knowing a priori the correct number of clusters. © 2010 Wiley Periodicals, Inc.
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paper_title: A probabilistic similarity metric for medline records: A model for author name disambiguation

paper_content:

We present a model for estimating the probability that a pair of author names (sharing last name and first initial), appearing on two different Medline articles, refer to the same individual. The model uses a simple yet powerful similarity profile between a pair of articles, based on title, journal name, coauthor names, medical subject headings (MeSH), language, affiliation, and name attributes (prevalence in the literature, middle initial, and suffix). The similarity profile distribution is computed from reference sets consisting of pairs of articles containing almost exclusively author matches versus nonmatches, generated in an unbiased manner. Although the match set is generated automatically and might contain a small proportion of nonmatches, the model is quite robust against contamination with nonmatches. We have created a free, public service (“Author-ity”: http://arrowsmith.psych.uic.edu) that takes as input an author’s name given on a specific article, and gives as output a list of all articles with that (last name, first initial) ranked by decreasing similarity, with match probability indicated.
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paper_title: Name disambiguation in author citations using a K-way spectral clustering method

paper_content:

An author may have multiple names and multiple authors may share the same name simply due to name abbreviations, identical names, or name misspellings in publications or bibliographies (citations). This can produce name ambiguity which can affect the performance of document retrieval, web search, and database integration, and may cause improper attribution of credit. Proposed here is an unsupervised learning approach using K-way spectral clustering that disambiguates authors in citations. The approach utilizes three types of citation attributes: co-author names, paper titles, and publication venue titles. The approach is illustrated with 16 name datasets with citations collected from the DBLP database bibliography and author home pages and shows that name disambiguation can be achieved using these citation attributes
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paper_title: IntelliClean: a knowledge-based intelligent data cleaner

paper_content:

data cleaning methods work on the basis of com- puting the degree of similarity between nearby records in a sorted database. High recall is achieved by accepting records with low degrees of similarity as duplicates, at the cost of lower precision. High precision is achieved analogously at the cost of lower recall. This is the recall-precision dilemma. In this paper, we propose a generic knowledge-based frame- work for effective data cleaning that implements existing cleaning strategies and more. We develop a new method to compute transitive closure under uncertainty which handles the merging of groups of inexact duplicate records. Experi- mental results show that this framework can identify dupli- cates and anomalies with high recall and precision.
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paper_title: Separating the articles of authors with the same name

paper_content:

I describe a method to separate the articles of different authors with the same name. It is based on a distance between any two publications, defined in terms of the probability that they would have as many coincidences if they were drawn at random from all published documents. Articles with a given author name are then clustered according to their distance, so that all articles in a cluster belong very likely to the same author. The method has proven very useful in generating groups of papers that are then selected manually. This simplifies considerably citation analysis when the author publication lists are not available.
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paper_title: Evaluating the Use of Social Networks in Author Name Disambiguation in Digital Libraries

paper_content:

Improvements to lighting systems, especially for entertainment and architectural applications are disclosed, including apparatus that permit a user to readily field-configure power distribution to branch circuits to be dimmed or un-dimmed, single- or multi-phase, as required; communication of data over existing power wiring by variations in output of dimmers; and improved methods for generating, managing, and distributing data specifying the physical and electrical configurations of a lighting system.
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paper_title: Collective entity resolution in relational data

paper_content:

Many databases contain uncertain and imprecise references to real-world entities. The absence of identifiers for the underlying entities often results in a database which contains multiple references to the same entity. This can lead not only to data redundancy, but also inaccuracies in query processing and knowledge extraction. These problems can be alleviated through the use of entity resolution. Entity resolution involves discovering the underlying entities and mapping each database reference to these entities. Traditionally, entities are resolved using pairwise similarity over the attributes of references. However, there is often additional relational information in the data. Specifically, references to different entities may cooccur. In these cases, collective entity resolution, in which entities for cooccurring references are determined jointly rather than independently, can improve entity resolution accuracy. We propose a novel relational clustering algorithm that uses both attribute and relational information for determining the underlying domain entities, and we give an efficient implementation. We investigate the impact that different relational similarity measures have on entity resolution quality. We evaluate our collective entity resolution algorithm on multiple real-world databases. We show that it improves entity resolution performance over both attribute-based baselines and over algorithms that consider relational information but do not resolve entities collectively. In addition, we perform detailed experiments on synthetically generated data to identify data characteristics that favor collective relational resolution over purely attribute-based algorithms.
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paper_title: A Comparison of String Distance Metrics for Name-Matching Tasks

paper_content:

Using an open-source, Java toolkit of name-matching methods, we experimentally compare string distance metrics on the task of matching entity names. We investigate a number of different metrics proposed by different communities, including edit-distance metrics, fast heuristic string comparators, token-based distance metrics, and hybrid methods. Overall, the best-performing method is a hybrid scheme combining a TFIDF weighting scheme, which is widely used in information retrieval, with the Jaro-Winkler string-distance scheme, which was developed in the probabilistic record linkage community.
---
paper_title: Separating the articles of authors with the same name

paper_content:

I describe a method to separate the articles of different authors with the same name. It is based on a distance between any two publications, defined in terms of the probability that they would have as many coincidences if they were drawn at random from all published documents. Articles with a given author name are then clustered according to their distance, so that all articles in a cluster belong very likely to the same author. The method has proven very useful in generating groups of papers that are then selected manually. This simplifies considerably citation analysis when the author publication lists are not available.
---
paper_title: Evaluating the Use of Social Networks in Author Name Disambiguation in Digital Libraries

paper_content:

Improvements to lighting systems, especially for entertainment and architectural applications are disclosed, including apparatus that permit a user to readily field-configure power distribution to branch circuits to be dimmed or un-dimmed, single- or multi-phase, as required; communication of data over existing power wiring by variations in output of dimmers; and improved methods for generating, managing, and distributing data specifying the physical and electrical configurations of a lighting system.
---
paper_title: Data Mining Concepts and Techniques

paper_content:

Understand the need for analyses of large, complex, information-rich data sets. Identify the goals and primary tasks of the data-mining process. Describe the roots of data-mining technology. Recognize the iterative character of a data-mining process and specify its basic steps. Explain the influence of data quality on a data-mining process. Establish the relation between data warehousing and data mining. Data mining is an iterative process within which progress is defined by discovery, through either automatic or manual methods. Data mining is most useful in an exploratory analysis scenario in which there are no predetermined notions about what will constitute an "interesting" outcome. Data mining is the search for new, valuable, and nontrivial information in large volumes of data. It is a cooperative effort of humans and computers. Best results are achieved by balancing the knowledge of human experts in describing problems and goals with the search capabilities of computers. In practice, the two primary goals of data mining tend to be prediction and description. Prediction involves using some variables or fields in the data set to predict unknown or future values of other variables of interest. Description, on the other hand, focuses on finding patterns describing the data that can be interpreted by humans. Therefore, it is possible to put data-mining activities into one of two categories: Predictive data mining, which produces the model of the system described by the given data set, or Descriptive data mining, which produces new, nontrivial information based on the available data set.
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paper_title: Spectral Relaxation for K means Clustering

paper_content:

The popular K-means clustering partitions a data set by minimizing a sum-of-squares cost function. A coordinate descend method is then used to find local minima. In this paper we show that the minimization can be reformulated as a trace maximization problem associated with the Gram matrix of the data vectors. Furthermore, we show that a relaxed version of the trace maximization problem possesses global optimal solutions which can be obtained by computing a partial eigendecomposition of the Gram matrix, and the cluster assignment for each data vectors can be found by computing a pivoted QR decomposition of the eigenvector matrix. As a by-product we also derive a lower bound for the minimum of the sum-of-squares cost function.
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paper_title: Cost-effective on-demand associative author name disambiguation

paper_content:

Authorship disambiguation is an urgent issue that affects the quality of digital library services and for which supervised solutions have been proposed, delivering state-of-the-art effectiveness. However, particular challenges such as the prohibitive cost of labeling vast amounts of examples (there are many ambiguous authors), the huge hypothesis space (there are several features and authors from which many different disambiguation functions may be derived), and the skewed author popularity distribution (few authors are very prolific, while most appear in only few citations), may prevent the full potential of such techniques. In this article, we introduce an associative author name disambiguation approach that identifies authorship by extracting, from training examples, rules associating citation features (e.g., coauthor names, work title, publication venue) to specific authors. As our main contribution we propose three associative author name disambiguators: (1) EAND (Eager Associative Name Disambiguation), our basic method that explores association rules for name disambiguation; (2) LAND (Lazy Associative Name Disambiguation), that extracts rules on a demand-driven basis at disambiguation time, reducing the hypothesis space by focusing on examples that are most suitable for the task; and (3) SLAND (Self-Training LAND), that extends LAND with self-training capabilities, thus drastically reducing the amount of examples required for building effective disambiguation functions, besides being able to detect novel/unseen authors in the test set. Experiments demonstrate that all our disambigutators are effective and that, in particular, SLAND is able to outperform state-of-the-art supervised disambiguators, providing gains that range from 12% to more than 400%, being extremely effective and practical.
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paper_title: Maximum likelihood from incomplete data via the EM algorithm

paper_content:

Vibratory power unit for vibrating conveyers and screens comprising an asynchronous polyphase motor, at least one pair of associated unbalanced masses disposed on the shaft of said motor, with the first mass of a pair of said unbalanced masses being rigidly fastened to said shaft and with said second mass of said pair being movably arranged relative to said first mass, means for controlling and regulating the conveying rate during conveyer operation by varying the rotational speed of said motor between predetermined minimum and maximum values, said second mass being movably outwardly by centrifugal force against the pressure of spring means, said spring means being prestressed in such a manner that said second mass is, at rotational motor speeds lower than said minimum speed, held in its initial position, and at motor speeds between said lower and upper values in positions which are radially offset with respect to the axis of said motor to an extent depending on the value of said rotational motor speed.
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paper_title: Effective self-training author name disambiguation in scholarly digital libraries

paper_content:

Name ambiguity in the context of bibliographic citation records is a hard problem that affects the quality of services and content in digital libraries and similar systems. Supervised methods that exploit training examples in order to distinguish ambiguous author names are among the most effective solutions for the problem, but they require skilled human annotators in a laborious and continuous process of manually labeling citations in order to provide enough training examples. Thus, addressing the issues of (i) automatic acquisition of examples and (ii) highly effective disambiguation even when only few examples are available, are the need of the hour for such systems. In this paper, we propose a novel two-step disambiguation method, SAND (Self-training Associative Name Disambiguator), that deals with these two issues. The first step eliminates the need of any manual labeling effort by automatically acquiring examples using a clustering method that groups citation records based on the similarity among coauthor names. The second step uses a supervised disambiguation method that is able to detect unseen authors not included in any of the given training examples. Experiments conducted with standard public collections, using the minimum set of attributes present in a citation (i.e., author names, work title and publication venue), demonstrated that our proposed method outperforms representative unsupervised disambiguation methods that exploit similarities between citation records and is as effective as, and in some cases superior to, supervised ones, without manually labeling any training example.
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paper_title: On Graph-Based Name Disambiguation

paper_content:

Name ambiguity stems from the fact that many people or objects share identical names in the real world. Such name ambiguity decreases the performance of document retrieval, Web search, information integration, and may cause confusion in other applications. Due to the same name spellings and lack of information, it is a nontrivial task to distinguish them accurately. In this article, we focus on investigating the problem in digital libraries to distinguish publications written by authors with identical names. We present an effective framework named GHOST (abbreviation for GrapHical framewOrk for name diSambiguaTion), to solve the problem systematically. We devise a novel similarity metric, and utilize only one type of attribute (i.e., coauthorship) in GHOST. Given the similarity matrix, intermediate results are grouped into clusters with a recently introduced powerful clustering algorithm called Affinity Propagation. In addition, as a complementary technique, user feedback can be used to enhance the performance. We evaluated the framework on the real DBLP and PubMed datasets, and the experimental results show that GHOST can achieve both high precision and recall.
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paper_title: A hierarchical naive Bayes mixture model for name disambiguation in author citations

paper_content:

Because of name variations, an author may have multiple names and multiple authors may share the same name. Such name ambiguity affects the performance of document retrieval, web search, database integration, and may cause improper attribution to authors. This paper presents a hierarchical naive Bayes mixture model, an unsupervised learning approach, for name disambiguation in author citations. This method partitions a collection of citations1 into clusters, with each cluster containing only citations authored by the same author, thus disambiguating authorship in citations to induce author name identities. Three types of citation features are used: co-author names, paper title words, and journal or proceeding title words. The approach is illustrated with 16 name datasets that are constructed based on the publication lists collected from author homepages and DBLP computer science bibliography.
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paper_title: Two supervised learning approaches for name disambiguation in author citations

paper_content:

Due to name abbreviations, identical names, name misspellings, and pseudonyms in publications or bibliographies (citations), an author may have multiple names and multiple authors may share the same name. Such name ambiguity affects the performance of document retrieval, Web search, database integration, and may cause improper attribution to authors. We investigate two supervised learning approaches to disambiguate authors in the citations. One approach uses the naive Bayes probability model, a generative model; the other uses support vector machines (SVMs) [V. Vapnik (1995)] and the vector space representation of citations, a discriminative model. Both approaches utilize three types of citation attributes: coauthor names, the title of the paper, and the title of the journal or proceeding. We illustrate these two approaches on two types of data, one collected from the Web, mainly publication lists from homepages, the other collected from the DBLP citation databases.
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paper_title: A Unified Probabilistic Framework for Name Disambiguation in Digital Library

paper_content:

Despite years of research, the name ambiguity problem remains largely unresolved. Outstanding issues include how to capture all information for name disambiguation in a unified approach, and how to determine the number of people K in the disambiguation process. In this paper, we formalize the problem in a unified probabilistic framework, which incorporates both attributes and relationships. Specifically, we define a disambiguation objective function for the problem and propose a two-step parameter estimation algorithm. We also investigate a dynamic approach for estimating the number of people K. Experiments show that our proposed framework significantly outperforms four baseline methods of using clustering algorithms and two other previous methods. Experiments also indicate that the number K automatically found by our method is close to the actual number.
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paper_title: A Latent Dirichlet Model for Unsupervised Entity Resolution

paper_content:

Entity resolution has received considerable attention in recent years. Given many references to underlying entities, the goal is to predict which references correspond to the same entity. We show how to extend the Latent Dirichlet Allocation model for this task and propose a probabilistic model for collective entity resolution for relational domains where references are connected to each other. Our approach differs from other recently proposed entity resolution approaches in that it is a) generative, b) does not make pair-wise decisions and c) captures relations between entities through a hidden group variable. We propose a novel sampling algorithm for collective entity resolution which is unsupervised and also takes entity relations into account. Additionally, we do not assume the domain of entities to be known and show how to infer the number of entities from the data. We demonstrate the utility and practicality of our relational entity resolution approach for author resolution in two real-world bibliographic datasets. In addition, we present preliminary results on characterizing conditions under which relational information is useful.
---
paper_title: Ultraconservative online algorithms for multiclass problems

paper_content:

In this paper we study a paradigm to generalize online classification algorithms for binary classification problems to multiclass problems. The particular hypotheses we investigate maintain one prototype vector per class. Given an input instance, a multiclass hypothesis computes a similarity-score between each prototype and the input instance and sets the predicted label to be the index of the prototype achieving the highest similarity. To design and analyze the learning algorithms in this paper we introduce the notion of ultraconservativeness. Ultraconservative algorithms are algorithms that update only the prototypes attaining similarity-scores which are higher than the score of the correct label's prototype. We start by describing a family of additive ultraconservative algorithms where each algorithm in the family updates its prototypes by finding a feasible solution for a set of linear constraints that depend on the instantaneous similarity-scores. We then discuss a specific online algorithm that seeks a set of prototypes which have a small norm. The resulting algorithm, which we term MIRA (for Margin Infused Relaxed Algorithm) is ultraconservative as well. We derive mistake bounds for all the algorithms and provide further analysis of MIRA using a generalized notion of the margin for multiclass problems. We discuss the form the algorithms take in the binary case and show that all the algorithms from the first family reduce to the Perceptron algorithm while MIRA provides a new Perceptron-like algorithm with a margin-dependent learning rate. We then return to multiclass problems and describe an analogous multiplicative family of algorithms with corresponding mistake bounds. We end the formal part by deriving and analyzing a multiclass version of Li and Long's ROMMA algorithm. We conclude with a discussion of experimental results that demonstrate the merits of our algorithms.
---
paper_title: An unsupervised heuristic-based hierarchical method for name disambiguation in bibliographic citations

paper_content:

Name ambiguity in the context of bibliographic citations is a difficult problem which, despite the many efforts from the research community, still has a lot of room for improvement. In this article, we present a heuristic-based hierarchical clustering method to deal with this problem. The method successively fuses clusters of citations of similar author names based on several heuristics and similarity measures on the components of the citations (e.g., coauthor names, work title, and publication venue title). During the disambiguation task, the information about fused clusters is aggregated providing more information for the next round of fusion. In order to demonstrate the effectiveness of our method, we ran a series of experiments in two different collections extracted from real-world digital libraries and compared it, under two metrics, with four representative methods described in the literature. We present comparisons of results using each considered attribute separately (i.e., coauthor names, work title, and publication venue title) with the author name attribute and using all attributes together. These results show that our unsupervised method, when using all attributes, performs competitively against all other methods, under both metrics, loosing only in one case against a supervised method, whose result was very close to ours. Moreover, such results are achieved without the burden of any training and without using any privileged information such as knowing a priori the correct number of clusters. © 2010 Wiley Periodicals, Inc.
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paper_title: Improving Author Coreference by Resource-Bounded Information Gathering from the Web

paper_content:

Accurate entity resolution is sometimes impossible simply due to insufficient information. For example, in research paper author name resolution, even clever use of venue, title and coauthorship relations are often not enough to make a confident coreference decision. This paper presents several methods for increasing accuracy by gathering and integrating additional evidence from the web. We formulate the coreference problem as one of graph partitioning with discriminatively-trained edge weights, and then incorporate web information either as additional features or as additional nodes in the graph. Since the web is too large to incorporate all its data, we need an efficient procedure for selecting a subset of web queries and data. We formally describe the problem of resource bounded information gathering in each of these contexts, and show significant accuracy improvement with low cost.
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paper_title: On co-authorship for author disambiguation

paper_content:

Author name disambiguation deals with clustering the same-name authors into different individuals. To attack the problem, many studies have employed a variety of disambiguation features such as coauthors, titles of papers/publications, topics of articles, emails/affiliations, etc. Among these, co-authorship is the most easily accessible and influential, since inter-person acquaintances represented by co-authorship could discriminate the identities of authors more clearly than other features. This study attempts to explore the net effects of co-authorship on author clustering in bibliographic data. First, to handle the shortage of explicit coauthors listed in known citations, a web-assisted technique of acquiring implicit coauthors of the target author to be disambiguated is proposed. Then, a coauthor disambiguation hypothesis that the identity of an author can be determined by his/her coauthors is examined and confirmed through a variety of author disambiguation experiments.
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paper_title: Author Name Disambiguation for Citations Using Topic and Web Correlation

paper_content:

Today, bibliographic digital libraries play an important role in helping members of academic community search for novel research. In particular, author disambiguation for citations is a major problem during the data integration and cleaning process, since author names are usually very ambiguous. For solving this problem, we proposed two kinds of correlations between citations, namely, Topic Correlationand Web Correlation, to exploit relationships between citations, in order to identify whether two citations with the same author name refer to the same individual.The topic correlation measures the similarity between research topics of two citations; while the Web correlation measures the number of co-occurrence in web pages. We employ a pair-wise grouping algorithm to group citations into clusters. The results of experiments show that the disambiguation accuracy has great improvement when using topic correlation and Web correlation, and Web correlation provides stronger evidences about the authors of citations.
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paper_title: A Latent Topic Model for Complete Entity Resolution

paper_content:

In bibliographies like DBLP and Citeseer, there are three kinds of entity-name problems that need to be solved. First, multiple entities share one name, which is called the name sharing problem. Second, one entity has different names, which is called the name variant problem. Third, multiple entities share multiple names, which is called the name mixing problem. We aim to solve these problems based on one model in this paper. We call this task complete entity resolution. Different from previous work, our work use global information based on data with two types of information, words and author names. We propose a generative latent topic model that involves both author names and words — the LDA-dual model, by extending the LDA (Latent Dirichlet Allocation) model. We also propose a method to obtain model parameters that is global information. Based on obtained model parameters, we propose two algorithms to solve the three problems mentioned above. Experimental results demonstrate the effectiveness and great potential of the proposed model and algorithms.
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paper_title: Two supervised learning approaches for name disambiguation in author citations

paper_content:

Due to name abbreviations, identical names, name misspellings, and pseudonyms in publications or bibliographies (citations), an author may have multiple names and multiple authors may share the same name. Such name ambiguity affects the performance of document retrieval, Web search, database integration, and may cause improper attribution to authors. We investigate two supervised learning approaches to disambiguate authors in the citations. One approach uses the naive Bayes probability model, a generative model; the other uses support vector machines (SVMs) [V. Vapnik (1995)] and the vector space representation of citations, a discriminative model. Both approaches utilize three types of citation attributes: coauthor names, the title of the paper, and the title of the journal or proceeding. We illustrate these two approaches on two types of data, one collected from the Web, mainly publication lists from homepages, the other collected from the DBLP citation databases.
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paper_title: Efficient topic-based unsupervised name disambiguation

paper_content:

Name ambiguity is a special case of identity uncertainty where one person can be referenced by multiple name variations in different situations or even share the same name with other people. In this paper, we focus on the problem of disambiguating person names within web pages and scientific documents. We present an efficient and effective two-stage approach to disambiguate names. In the first stage, two novel topic-based models are proposed by extending two hierarchical Bayesian text models, namely Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA). Our models explicitly introduce a new variable for persons and learn the distribution of topics with regard to persons and words. After learning an initial model, the topic distributions are treated as feature sets and names are disambiguated by leveraging a hierarchical agglomerative clustering method. Experiments on web data and scientific documents from CiteSeer indicate that our approach consistently outperforms other unsupervised learning methods such as spectral clustering and DBSCAN clustering and could be extended to other research fields. We empirically addressed the issue of scalability by disambiguating authors in over 750,000 papers from the entire CiteSeer dataset.
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paper_title: Data clustering: a review

paper_content:

Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overviewof pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.
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paper_title: Using web information for author name disambiguation

paper_content:

In digital libraries, ambiguous author names may occur due to the existence of multiple authors with the same name (polysemes) or different name variations for the same author (synonyms). We proposed here a new method that uses information available on the Web to deal with both problems at the same time. Our idea consists of gathering information from input citations and submitting queries to a Web search engine, aiming at finding curricula vitae and Web pages containing publications of the ambiguous authors. From the content of documents in the answer sets returned by the Web search engine, useful information that can help in the disambiguation process is extracted. Using this information, author names are disambiguated by leveraging a hierarchical clustering method that groups citations in the same document together in a bottom-up fashion. Experimental results show that the our method yields results that outperform those of two state-of-the-art unsupervised methods and are statistically comparable with those of a supervised one, but requiring no training. We observe gains of up to 65.2% in the pairwise F1 metric when compared with our best unsupervised baseline method.
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paper_title: Improving Author Coreference by Resource-Bounded Information Gathering from the Web

paper_content:

Accurate entity resolution is sometimes impossible simply due to insufficient information. For example, in research paper author name resolution, even clever use of venue, title and coauthorship relations are often not enough to make a confident coreference decision. This paper presents several methods for increasing accuracy by gathering and integrating additional evidence from the web. We formulate the coreference problem as one of graph partitioning with discriminatively-trained edge weights, and then incorporate web information either as additional features or as additional nodes in the graph. Since the web is too large to incorporate all its data, we need an efficient procedure for selecting a subset of web queries and data. We formally describe the problem of resource bounded information gathering in each of these contexts, and show significant accuracy improvement with low cost.
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paper_title: On co-authorship for author disambiguation

paper_content:

Author name disambiguation deals with clustering the same-name authors into different individuals. To attack the problem, many studies have employed a variety of disambiguation features such as coauthors, titles of papers/publications, topics of articles, emails/affiliations, etc. Among these, co-authorship is the most easily accessible and influential, since inter-person acquaintances represented by co-authorship could discriminate the identities of authors more clearly than other features. This study attempts to explore the net effects of co-authorship on author clustering in bibliographic data. First, to handle the shortage of explicit coauthors listed in known citations, a web-assisted technique of acquiring implicit coauthors of the target author to be disambiguated is proposed. Then, a coauthor disambiguation hypothesis that the identity of an author can be determined by his/her coauthors is examined and confirmed through a variety of author disambiguation experiments.
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paper_title: Author name disambiguation in MEDLINE

paper_content:

Background: We recently described “Author-ity,” a model for estimating the probability that two articles in MEDLINE, sharing the same author name, were written by the same individual. Features include shared title words, journal name, coauthors, medical subject headings, language, affiliations, and author name features (middle initial, suffix, and prevalence in MEDLINE). Here we test the hypothesis that the Author-ity model will suffice to disambiguate author names for the vast majority of articles in MEDLINE. Methods: Enhancements include: (a) incorporating first names and their variants, email addresses, and correlations between specific last names and affiliation words; (b) new methods of generating large unbiased training sets; (c) new methods for estimating the prior probability; (d) a weighted least squares algorithm for correcting transitivity violations; and (e) a maximum likelihood based agglomerative algorithm for computing clusters of articles that represent inferred author-individuals. Results: Pairwise comparisons were computed for all author names on all 15.3 million articles in MEDLINE (2006 baseline), that share last name and first initial, to create Author-ity 2006, a database that has each name on each article assigned to one of 6.7 million inferred author-individual clusters. Recall is estimated at ∼98.8p. Lumping (putting two different individuals into the same cluster) affects ∼0.5p of clusters, whereas splitting (assigning articles written by the same individual to >1 cluster) affects ∼2p of articles. Impact: The Author-ity model can be applied generally to other bibliographic databases. Author name disambiguation allows information retrieval and data integration to become person-centered, not just document-centered, setting the stage for new data mining and social network tools that will facilitate the analysis of scholarly publishing and collaboration behavior. Availability: The Author-ity 2006 database is available for nonprofit academic research, and can be freely queried via http://arrowsmith.psych.uic.edu.
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paper_title: Cost-effective on-demand associative author name disambiguation

paper_content:

Authorship disambiguation is an urgent issue that affects the quality of digital library services and for which supervised solutions have been proposed, delivering state-of-the-art effectiveness. However, particular challenges such as the prohibitive cost of labeling vast amounts of examples (there are many ambiguous authors), the huge hypothesis space (there are several features and authors from which many different disambiguation functions may be derived), and the skewed author popularity distribution (few authors are very prolific, while most appear in only few citations), may prevent the full potential of such techniques. In this article, we introduce an associative author name disambiguation approach that identifies authorship by extracting, from training examples, rules associating citation features (e.g., coauthor names, work title, publication venue) to specific authors. As our main contribution we propose three associative author name disambiguators: (1) EAND (Eager Associative Name Disambiguation), our basic method that explores association rules for name disambiguation; (2) LAND (Lazy Associative Name Disambiguation), that extracts rules on a demand-driven basis at disambiguation time, reducing the hypothesis space by focusing on examples that are most suitable for the task; and (3) SLAND (Self-Training LAND), that extends LAND with self-training capabilities, thus drastically reducing the amount of examples required for building effective disambiguation functions, besides being able to detect novel/unseen authors in the test set. Experiments demonstrate that all our disambigutators are effective and that, in particular, SLAND is able to outperform state-of-the-art supervised disambiguators, providing gains that range from 12% to more than 400%, being extremely effective and practical.
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paper_title: Improving Grouped-Entity Resolution Using Quasi-Cliques

paper_content:

The entity resolution (ER) problem, which identifies duplicate entities that refer to the same real world entity, is essential in many applications. In this paper, in particular, we focus on resolving entities that contain a group of related elements in them (e.g., an author entity with a list of citations, a singer entity with song list, or an intermediate result by GROUP BY SQL query). Such entities, named as grouped-entities, frequently occur in many applications. The previous approaches toward grouped-entity resolution often rely on textual similarity, and produce a large number of false positives. As a complementing technique, in this paper, we present our experience of applying a recently proposed graph mining technique, Quasi-Clique, atop conventional ER solutions. Our approach exploits contextual information mined from the group of elements per entity in addition to syntactic similarity. Extensive experiments verify that our proposal improves precision and recall up to 83% when used together with a variety of existing ER solutions, but never worsens them.
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paper_title: Author Name Disambiguation for Citations Using Topic and Web Correlation

paper_content:

Today, bibliographic digital libraries play an important role in helping members of academic community search for novel research. In particular, author disambiguation for citations is a major problem during the data integration and cleaning process, since author names are usually very ambiguous. For solving this problem, we proposed two kinds of correlations between citations, namely, Topic Correlationand Web Correlation, to exploit relationships between citations, in order to identify whether two citations with the same author name refer to the same individual.The topic correlation measures the similarity between research topics of two citations; while the Web correlation measures the number of co-occurrence in web pages. We employ a pair-wise grouping algorithm to group citations into clusters. The results of experiments show that the disambiguation accuracy has great improvement when using topic correlation and Web correlation, and Web correlation provides stronger evidences about the authors of citations.
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paper_title: Effective self-training author name disambiguation in scholarly digital libraries

paper_content:

Name ambiguity in the context of bibliographic citation records is a hard problem that affects the quality of services and content in digital libraries and similar systems. Supervised methods that exploit training examples in order to distinguish ambiguous author names are among the most effective solutions for the problem, but they require skilled human annotators in a laborious and continuous process of manually labeling citations in order to provide enough training examples. Thus, addressing the issues of (i) automatic acquisition of examples and (ii) highly effective disambiguation even when only few examples are available, are the need of the hour for such systems. In this paper, we propose a novel two-step disambiguation method, SAND (Self-training Associative Name Disambiguator), that deals with these two issues. The first step eliminates the need of any manual labeling effort by automatically acquiring examples using a clustering method that groups citation records based on the similarity among coauthor names. The second step uses a supervised disambiguation method that is able to detect unseen authors not included in any of the given training examples. Experiments conducted with standard public collections, using the minimum set of attributes present in a citation (i.e., author names, work title and publication venue), demonstrated that our proposed method outperforms representative unsupervised disambiguation methods that exploit similarities between citation records and is as effective as, and in some cases superior to, supervised ones, without manually labeling any training example.
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paper_title: On Graph-Based Name Disambiguation

paper_content:

Name ambiguity stems from the fact that many people or objects share identical names in the real world. Such name ambiguity decreases the performance of document retrieval, Web search, information integration, and may cause confusion in other applications. Due to the same name spellings and lack of information, it is a nontrivial task to distinguish them accurately. In this article, we focus on investigating the problem in digital libraries to distinguish publications written by authors with identical names. We present an effective framework named GHOST (abbreviation for GrapHical framewOrk for name diSambiguaTion), to solve the problem systematically. We devise a novel similarity metric, and utilize only one type of attribute (i.e., coauthorship) in GHOST. Given the similarity matrix, intermediate results are grouped into clusters with a recently introduced powerful clustering algorithm called Affinity Propagation. In addition, as a complementary technique, user feedback can be used to enhance the performance. We evaluated the framework on the real DBLP and PubMed datasets, and the experimental results show that GHOST can achieve both high precision and recall.
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paper_title: A Latent Topic Model for Complete Entity Resolution

paper_content:

In bibliographies like DBLP and Citeseer, there are three kinds of entity-name problems that need to be solved. First, multiple entities share one name, which is called the name sharing problem. Second, one entity has different names, which is called the name variant problem. Third, multiple entities share multiple names, which is called the name mixing problem. We aim to solve these problems based on one model in this paper. We call this task complete entity resolution. Different from previous work, our work use global information based on data with two types of information, words and author names. We propose a generative latent topic model that involves both author names and words — the LDA-dual model, by extending the LDA (Latent Dirichlet Allocation) model. We also propose a method to obtain model parameters that is global information. Based on obtained model parameters, we propose two algorithms to solve the three problems mentioned above. Experimental results demonstrate the effectiveness and great potential of the proposed model and algorithms.
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paper_title: A hierarchical naive Bayes mixture model for name disambiguation in author citations

paper_content:

Because of name variations, an author may have multiple names and multiple authors may share the same name. Such name ambiguity affects the performance of document retrieval, web search, database integration, and may cause improper attribution to authors. This paper presents a hierarchical naive Bayes mixture model, an unsupervised learning approach, for name disambiguation in author citations. This method partitions a collection of citations1 into clusters, with each cluster containing only citations authored by the same author, thus disambiguating authorship in citations to induce author name identities. Three types of citation features are used: co-author names, paper title words, and journal or proceeding title words. The approach is illustrated with 16 name datasets that are constructed based on the publication lists collected from author homepages and DBLP computer science bibliography.
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paper_title: Collective entity resolution in relational data

paper_content:

Many databases contain uncertain and imprecise references to real-world entities. The absence of identifiers for the underlying entities often results in a database which contains multiple references to the same entity. This can lead not only to data redundancy, but also inaccuracies in query processing and knowledge extraction. These problems can be alleviated through the use of entity resolution. Entity resolution involves discovering the underlying entities and mapping each database reference to these entities. Traditionally, entities are resolved using pairwise similarity over the attributes of references. However, there is often additional relational information in the data. Specifically, references to different entities may cooccur. In these cases, collective entity resolution, in which entities for cooccurring references are determined jointly rather than independently, can improve entity resolution accuracy. We propose a novel relational clustering algorithm that uses both attribute and relational information for determining the underlying domain entities, and we give an efficient implementation. We investigate the impact that different relational similarity measures have on entity resolution quality. We evaluate our collective entity resolution algorithm on multiple real-world databases. We show that it improves entity resolution performance over both attribute-based baselines and over algorithms that consider relational information but do not resolve entities collectively. In addition, we perform detailed experiments on synthetically generated data to identify data characteristics that favor collective relational resolution over purely attribute-based algorithms.
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paper_title: Two supervised learning approaches for name disambiguation in author citations

paper_content:

Due to name abbreviations, identical names, name misspellings, and pseudonyms in publications or bibliographies (citations), an author may have multiple names and multiple authors may share the same name. Such name ambiguity affects the performance of document retrieval, Web search, database integration, and may cause improper attribution to authors. We investigate two supervised learning approaches to disambiguate authors in the citations. One approach uses the naive Bayes probability model, a generative model; the other uses support vector machines (SVMs) [V. Vapnik (1995)] and the vector space representation of citations, a discriminative model. Both approaches utilize three types of citation attributes: coauthor names, the title of the paper, and the title of the journal or proceeding. We illustrate these two approaches on two types of data, one collected from the Web, mainly publication lists from homepages, the other collected from the DBLP citation databases.
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paper_title: Disambiguating authors in academic publications using random forests

paper_content:

Users of digital libraries usually want to know the exact author or authors of an article. But different authors may share the same names, either as full names or as initials and last names (complete name change examples are not considered here). In such a case, the user would like the digital library to differentiate among these authors. Name disambiguation can help in many cases; one being a user in a search of all articles written by a particular author. Disambiguation also enables better bibliometric analysis by allowing a more accurate counting and grouping of publications and citations. In this paper, we describe an algorithm for pair-wise disambiguation of author names based on a machine learning classification algorithm, random forests. We define a set of similarity profile features to assist in author disambiguation. Our experiments on the Medline database show that the random forest model outperforms other previously proposed techniques such as those using support-vector machines (SVM). In addition, we demonstrate that the variable importance produced by the random forest model can be used in feature selection with little degradation in the disambiguation accuracy. In particular, the inverse document frequency of author last name and the middle name's similarity alone achieves an accuracy of almost 90%.
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paper_title: Author Disambiguation using Error-driven Machine Learning with a Ranking Loss Function

paper_content:

Author disambiguation is the problem of determining whether records in a publications database refer to the same person. A common supervised machine learning approach is to build a classifier to predict whether a pair of records is coreferent, followed by a clustering step to enforce transitivity. However, this approach ignores powerful evidence obtainable by examining sets (rather than pairs) of records, such as the number of publications or co-authors an author has. In this paper we propose a representation that enables these first-order features over sets of records. We then propose a training algorithm well-suited to this representation that is (1) error-driven in that training examples are generated from incorrect predictions on the training data, and (2) rankbased in that the classifier induces a ranking over candidate predictions. We evaluate our algorithms on three author disambiguation datasets and demonstrate error reductions of up to 60% over the standard binary classification approach. Introduction Record deduplication is the problem of deciding whether two records in a database refer to the same object. This problem is widespread in any large-scale database, and is particularly acute when records are constructed automatically from text mining. Author disambiguation, the problem of deduplicating author records, is a critical concern for digital publication libraries such as Citeseer, DBLP, Rexa, and Google Scholar. Author disambiguation is difficult in these domains because of abbreviations (e.g., Y. Smith) misspellings (e.g., Y. Smiht), and extraction errors (e.g., Smith Statistical). Many supervised machine learning approaches to author disambiguation have been proposed. Most of these are variants of the following recipe: (1) train a binary classifier to predict whether a pair of authors are duplicates, (2) apply the classifier to each pair of ambiguous authors, (3) combine the classification predictions to cluster the records into duplicate sets. Copyright c © 2007, American Association for Artificial Intelligence (www.aaai.org). All rights reserved. This approach can be quite accurate, and is attractive because it builds upon existing machine learning technology (e.g., classification and clustering). However, because the core of this approach is a classifier over record pairs, nowhere are aggregate features of an author modeled explicitly. That is, by restricting the model representation to evidence over pairs of authors, we cannot leverage evidence available from examining more than two records. For example, we would like to model the fact that authors are generally affiliated with only a few institutions, have only one or two different email addresses, and are unlikely to publish more than thirty publications in one year. None of these constraints can be captured with a pairwise classifier, which, for example, can only consider whether pairs of institutions or emails match. In this paper we propose a representation for author disambiguation that enables these aggregate constraints. The representation can be understood as a scoring function over a set of authors, indicating how likely it is that all members of the set are duplicates. While flexible, this new representation can make it difficult to estimate the model parameters from training data. We therefore propose a class of training algorithms to estimate the parameters of models adopting this representation. The method has two main characteristics essential to its performance. First, it is errordriven in that training examples are generated based on mistakes made by the prediction algorithm. This approach focuses training effort on the types of examples expected during prediction. Second, it is rank-based in that the loss function induces a ranking over candidate predictions. By representing the difference between predictions, preferences can be expressed over partially-correct or incomplete solutions; additionally, intractable normalization constants can be avoided because the loss function is a ratio of terms. In the following sections, we describe the representation in more detail, then present our proposed training methods. We evaluate our proposals on three real-world author deduplication datasets and demonstrate errorreductions of up to 60%. Author Title Institution Year Y. Li Understanding Social Networks Stanford 2003 Y. Li Understanding Network Protocols Carnegie Mellon 2002 Y. Li Virtual Network Protocols Peking Univ. 2001 Table 1: Author disambiguation example with multiple institutions. Author Co-authors Title P. Cohen A. Howe How evaluation guides AI research P. Cohen M. Greenberg, A. Howe, ... Trial by Fire: Understanding the design requirements ... in complex environments P. Cohen M. Greenberg MU: a development environment for prospective reasoning systems Table 2: Author disambiguation example with overlapping co-authors.
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paper_title: A Unified Probabilistic Framework for Name Disambiguation in Digital Library

paper_content:

Despite years of research, the name ambiguity problem remains largely unresolved. Outstanding issues include how to capture all information for name disambiguation in a unified approach, and how to determine the number of people K in the disambiguation process. In this paper, we formalize the problem in a unified probabilistic framework, which incorporates both attributes and relationships. Specifically, we define a disambiguation objective function for the problem and propose a two-step parameter estimation algorithm. We also investigate a dynamic approach for estimating the number of people K. Experiments show that our proposed framework significantly outperforms four baseline methods of using clustering algorithms and two other previous methods. Experiments also indicate that the number K automatically found by our method is close to the actual number.
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paper_title: Efficient topic-based unsupervised name disambiguation

paper_content:

Name ambiguity is a special case of identity uncertainty where one person can be referenced by multiple name variations in different situations or even share the same name with other people. In this paper, we focus on the problem of disambiguating person names within web pages and scientific documents. We present an efficient and effective two-stage approach to disambiguate names. In the first stage, two novel topic-based models are proposed by extending two hierarchical Bayesian text models, namely Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA). Our models explicitly introduce a new variable for persons and learn the distribution of topics with regard to persons and words. After learning an initial model, the topic distributions are treated as feature sets and names are disambiguated by leveraging a hierarchical agglomerative clustering method. Experiments on web data and scientific documents from CiteSeer indicate that our approach consistently outperforms other unsupervised learning methods such as spectral clustering and DBSCAN clustering and could be extended to other research fields. We empirically addressed the issue of scalability by disambiguating authors in over 750,000 papers from the entire CiteSeer dataset.
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paper_title: An unsupervised heuristic-based hierarchical method for name disambiguation in bibliographic citations

paper_content:

Name ambiguity in the context of bibliographic citations is a difficult problem which, despite the many efforts from the research community, still has a lot of room for improvement. In this article, we present a heuristic-based hierarchical clustering method to deal with this problem. The method successively fuses clusters of citations of similar author names based on several heuristics and similarity measures on the components of the citations (e.g., coauthor names, work title, and publication venue title). During the disambiguation task, the information about fused clusters is aggregated providing more information for the next round of fusion. In order to demonstrate the effectiveness of our method, we ran a series of experiments in two different collections extracted from real-world digital libraries and compared it, under two metrics, with four representative methods described in the literature. We present comparisons of results using each considered attribute separately (i.e., coauthor names, work title, and publication venue title) with the author name attribute and using all attributes together. These results show that our unsupervised method, when using all attributes, performs competitively against all other methods, under both metrics, loosing only in one case against a supervised method, whose result was very close to ours. Moreover, such results are achieved without the burden of any training and without using any privileged information such as knowing a priori the correct number of clusters. © 2010 Wiley Periodicals, Inc.
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paper_title: Name disambiguation in author citations using a K-way spectral clustering method

paper_content:

An author may have multiple names and multiple authors may share the same name simply due to name abbreviations, identical names, or name misspellings in publications or bibliographies (citations). This can produce name ambiguity which can affect the performance of document retrieval, web search, and database integration, and may cause improper attribution of credit. Proposed here is an unsupervised learning approach using K-way spectral clustering that disambiguates authors in citations. The approach utilizes three types of citation attributes: co-author names, paper titles, and publication venue titles. The approach is illustrated with 16 name datasets with citations collected from the DBLP database bibliography and author home pages and shows that name disambiguation can be achieved using these citation attributes
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paper_title: Separating the articles of authors with the same name

paper_content:

I describe a method to separate the articles of different authors with the same name. It is based on a distance between any two publications, defined in terms of the probability that they would have as many coincidences if they were drawn at random from all published documents. Articles with a given author name are then clustered according to their distance, so that all articles in a cluster belong very likely to the same author. The method has proven very useful in generating groups of papers that are then selected manually. This simplifies considerably citation analysis when the author publication lists are not available.
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paper_title: A Latent Dirichlet Model for Unsupervised Entity Resolution

paper_content:

Entity resolution has received considerable attention in recent years. Given many references to underlying entities, the goal is to predict which references correspond to the same entity. We show how to extend the Latent Dirichlet Allocation model for this task and propose a probabilistic model for collective entity resolution for relational domains where references are connected to each other. Our approach differs from other recently proposed entity resolution approaches in that it is a) generative, b) does not make pair-wise decisions and c) captures relations between entities through a hidden group variable. We propose a novel sampling algorithm for collective entity resolution which is unsupervised and also takes entity relations into account. Additionally, we do not assume the domain of entities to be known and show how to infer the number of entities from the data. We demonstrate the utility and practicality of our relational entity resolution approach for author resolution in two real-world bibliographic datasets. In addition, we present preliminary results on characterizing conditions under which relational information is useful.
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paper_title: Evaluating the Use of Social Networks in Author Name Disambiguation in Digital Libraries

paper_content:

Improvements to lighting systems, especially for entertainment and architectural applications are disclosed, including apparatus that permit a user to readily field-configure power distribution to branch circuits to be dimmed or un-dimmed, single- or multi-phase, as required; communication of data over existing power wiring by variations in output of dimmers; and improved methods for generating, managing, and distributing data specifying the physical and electrical configurations of a lighting system.
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paper_title: Using web information for author name disambiguation

paper_content:

In digital libraries, ambiguous author names may occur due to the existence of multiple authors with the same name (polysemes) or different name variations for the same author (synonyms). We proposed here a new method that uses information available on the Web to deal with both problems at the same time. Our idea consists of gathering information from input citations and submitting queries to a Web search engine, aiming at finding curricula vitae and Web pages containing publications of the ambiguous authors. From the content of documents in the answer sets returned by the Web search engine, useful information that can help in the disambiguation process is extracted. Using this information, author names are disambiguated by leveraging a hierarchical clustering method that groups citations in the same document together in a bottom-up fashion. Experimental results show that the our method yields results that outperform those of two state-of-the-art unsupervised methods and are statistically comparable with those of a supervised one, but requiring no training. We observe gains of up to 65.2% in the pairwise F1 metric when compared with our best unsupervised baseline method.
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paper_title: Incremental Unsupervised Name Disambiguation in Cleaned Digital Libraries

paper_content:

Name ambiguity in the context of bibliographic citations is one of the hardest problems currently faced by the Digital Library (DL) community. Here we deal with the problem of disambiguating new citations records inserted into a cleaned DL, without the need to process the whole collection, which is usually necessary for unsupervised methods. Although supervised solutions can deal with this situation, there is the costly burden of generating training data besides the fact that these methods cannot handle well the insertion of records of new authors not already existent in the repository. In this article, we propose a new unsupervised method that identifies the correct authors of the new citation records to be inserted in a DL. The method is based on heuristics that are also used to identify whether the new records belong to authors already in the digital library or not, correctly identifying new authors in most cases. Our experimental evaluation, using synthetic and real datasets, shows gains of up to 19\% when compared to a state-of-the-art method without the cost of having to disambiguate the whole DL at each new load (as done by unsupervised methods) or the need for any training (as done by supervised methods).
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Title: A Brief Survey of Automatic Methods for Author Name Disambiguation
Section 1: INTRODUCTION
Description 1: Provide an overview of the problem of author name ambiguity in digital libraries, including examples and propositions for disambiguation.

Section 2: A TAXONOMY FOR AUTHOR NAME DISAMBIGUATION METHODS
Description 2: Present and describe a hierarchical taxonomy for categorizing author name disambiguation methods, focusing on different approaches used in the literature.

Section 3: Type of Approach
Description 3: Discuss various types of approaches for author name disambiguation, including author grouping and author assignment methods.

Section 4: Author Grouping Methods
Description 4: Elaborate on methods applying similarity functions for grouping references to the same author using clustering techniques.

Section 5: Author Assignment Methods
Description 5: Describe methods that directly assign each reference to a given author by constructing a model representing the author.

Section 6: Explored Evidence
Description 6: Detail the kinds of evidence most commonly explored by the disambiguation methods, including citation information, web information, and implicit evidence.

Section 7: Evaluation Metrics
Description 7: Explain the different metrics used for evaluating author name disambiguation methods and their effectiveness in experiments.

Section 8: OVERVIEW OF REPRESENTATIVE METHODS
Description 8: Provide an overview of representative author name disambiguation methods that fit into the proposed taxonomy, focusing on their unique techniques and effectiveness.

Section 9: Using Additional Evidence
Description 9: Discuss methods that use additional evidence beyond citation information, such as web information and implicit evidence.

Section 10: SUMMARY OF CHARACTERISTICS
Description 10: Summarize the characteristics and experimental evaluations of the described author name disambiguation methods.

Section 11: OPEN CHALLENGES
Description 11: Discuss the open challenges in the field of author name disambiguation, including issues related to data, ambiguity, errors, efficiency, and incremental disambiguation.