Concept-Based, Personalized Web Information Gathering: A Survey
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paper_title: Web Mining Research: A Survey

paper_content:

With the huge amount of information available online, the World Wide Web is a fertile area for data mining research. The Web mining research is at the cross road of research from several research communities, such as database, information retrieval, and within AI, especially the sub-areas of machine learning and natural language processing. However, there is a lot of confusions when comparing research efforts from different point of views. In this paper, we survey the research in the area of Web mining, point out some confusions regarded the usage of the term Web mining and suggest three Web mining categories. Then we situate some of the research with respect to these three categories. We also explore the connection between the Web mining categories and the related agent paradigm. For the survey, we focus on representation issues, on the process, on the learning algorithm, and on the application of the recent works as the criteria. We conclude the paper with some research issues.
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paper_title: Ontology-based personalized search and browsing

paper_content:

As the number of Internet users and the number of accessible Web pages grows, it is becoming increasingly difficult for users to find documents that are relevant to their particular needs. Users must either browse through a large hierarchy of concepts to find the information for which they are looking or submit a query to a publicly available search engine and wade through hundreds of results, most of them irrelevant. The core of the problem is that whether the user is browsing or searching, whether they are an eighth grade student or a Nobel prize winner, the identical information is selected and it is presented the same way. In this paper, we report on research that adapts information navigation based on a user profile structured as a weighted concept hierarchy. A user may create his or her own concept hierarchy and use them for browsing Web sites. Or, the user profile may be created from a reference ontology by 'watching over the user's shoulder' while they browse. We show that these automatically created profiles reflect the user's interests quite well and they are able to produce moderate improvements when applied to search results.
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paper_title: Improving Ontology-Based User Profiles

paper_content:

Personalized Web browsing and search hope to provide Web information that matches a user's personal interests and thus provide more effective and efficient information access. A key feature in developing successful personalized Web applications is to build user profiles that accurately represent a user's interests. The main goal of this research is to investigate techniques that implicitly build ontology-based user profiles. We build the profiles without user interaction, automatically monitoring the user's browsing habits. After building the initial profile from visited Web pages, we investigate techniques to improve the accuracy of the user profile. In particular, we focus on how quickly we can achieve profile stability, how to identify the most important concepts, the effect of depth in the concept-hierarchy on the importance of a concept, and how many levels from the hierarchy should be used to represent the user. ::: ::: Our major findings are that ranking the concepts in the profiles by number of documents assigned to them rather than by accumulated weights provides better profile accuracy. We are also able to identify stable concepts in the profile, thus allowing us to detect long-term user interests. We found that the accuracy of concept detection decreases as we descend them in the concept hierarchy, however this loss of accuracy must be balanced against the detailed view of the user available only through the inclusion of lower-level concepts.
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paper_title: REPRESENTATION AND CONSTRUCTION OF ONTOLOGIES FOR WEB INTELLIGENCE

paper_content:

Ontologies and agent technology can play a crucial role in enabling Web-based knowledge processing, sharing, and reuse between applications. The paper investigates representation and construction of ontologies for Web intelligence. Three ontology categories are suggested, some of the research and development with respect to the three categories is presented, the major ontology languages are surveyed, and a multi-phase process of automatic construction of the domain-specific ontologies is discussed.
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paper_title: Agent and ontology based information gathering on restricted web domains with AGATHE

paper_content:

Due to Web size and diversity of information, relevant information gathering on the Web is a very complex task. The main problem with most information retrieval approaches is neglecting the context of the pages, mainly because search engines are based on keyword indexing. Considering restricted domain, the policy of taking into account context may lead to more relevant information gathering in this paper, a specific cooperative information gathering approach based on the use of software agents and ontologies is proposed. To implement this approach, a generic software architecture, named AGATHE system, based on early prototype, the MASTER-Web system, permitting development of specific restricted-domain information gathering systems is presented in detail, with a focus on the extraction subsystem.
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paper_title: Information Fusion for Intelligent Agent-Based Information Gathering

paper_content:

This paper discusses the problem of information fusion for agent-based information gathering systems. The framework of information gathering in multi-agent environments is presented firstly, and then a cooperative fusion algorithm is presented for unstructured documents. The performance is also made by the traditional methods precision and recall, and it shows that the fusion algorithm is efficient.
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paper_title: Mining ontology for automatically acquiring Web user information needs

paper_content:

It is not easy to obtain the right information from the Web for a particular Web user or a group of users due to the obstacle of automatically acquiring Web user profiles. The current techniques do not provide satisfactory structures for mining Web user profiles. This paper presents a novel approach for this problem. The objective of the approach is to automatically discover ontologies from data sets in order to build complete concept models for Web user information needs. It also proposes a method for capturing evolving patterns to refine discovered ontologies. In addition, the process of assessing relevance in ontology is established. This paper provides both theoretical and experimental evaluations for the approach. The experimental results show that all objectives we expect for the approach are achievable.
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paper_title: A Semantic Web Primer

paper_content:

The development of the Semantic Web, with machine-readable content, has the potential to revolutionize the World Wide Web and its uses. A Semantic Web Primer provides an introduction and guide to this continuously evolving field, describing its key ideas, languages, and technologies. Suitable for use as a textbook or for independent study by professionals, it concentrates on undergraduate-level fundamental concepts and techniques that will enable readers to proceed with building applications on their own and includes exercises, project descriptions, and annotated references to relevant online materials.The third edition of this widely used text has been thoroughly updated, with significant new material that reflects a rapidly developing field. Treatment of the different languages (OWL2, rules) expands the coverage of RDF and OWL, defining the data model independently of XML and including coverage of N3/Turtle and RDFa. A chapter is devoted to OWL2, the new W3C standard. This edition also features additional coverage of the query language SPARQL, the rule language RIF and the possibility of interaction between rules and ontology languages and applications. The chapter on Semantic Web applications reflects the rapid developments of the past few years. A new chapter offers ideas for term projects. Additional material, including updates on the technological trends and research directions, can be found at http://www.semanticwebprimer.org.
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paper_title: Automatic feature thesaurus enrichment: extracting generic terms from digital gazetteer

paper_content:

ADL Gazetteer is a digitalized worldwide gazetteer developed in the Alexandria Digital Library (ADL) Project, which contains millions of geographic names (placenames). The placenames are indexed with type terms from the ADL Feature Type Thesaurus (FTT), a hierarchical category scheme. The paper proposes a two-step method to enrich the category scheme automatically: to discover frequent generic terms by detecting phase boundaries with a mutual information-based method, and to correlate the generic terms with the relevant type terms by hierarchical clustering. The correlation pair established can then be used to supplement the FTT with the generic terms found. The extensive experiments conducted on millions of ADLG placenames demonstrated the effectiveness of the proposed methods. Besides the thesaurus enrichment, the potential applications of this research include: to suggest likely type terms when categorizing new placenames, and to help users choose likely search terms.
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paper_title: Using WordNet to support interactive concept map construction

paper_content:

Concept mapping is a knowledge elicitation technique which stimulates learners to articulate and synthesize their actual states of knowledge during the learning process. Several approaches have been proposed for automating the assessment procedure of learners' concept maps based on an expert's map as a reference point. However, these approaches do not handle cases where learners have misspelled a concept or they have used a synonym or a concept related to the appropriate one. In this paper we present an alternative approach in which the process of the error identification is performed through the use of an expert map and of WordNet which is an electronic lexical database containing semantic relationships between words. This way we handle cases such as misspelled concepts, synonyms and related concepts. After error detection WordNet is also employed for providing the learner with appropriate feedback based on the identified errors, with the intention of helping the learner to correct them.
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paper_title: Ontology-based personalized search and browsing

paper_content:

As the number of Internet users and the number of accessible Web pages grows, it is becoming increasingly difficult for users to find documents that are relevant to their particular needs. Users must either browse through a large hierarchy of concepts to find the information for which they are looking or submit a query to a publicly available search engine and wade through hundreds of results, most of them irrelevant. The core of the problem is that whether the user is browsing or searching, whether they are an eighth grade student or a Nobel prize winner, the identical information is selected and it is presented the same way. In this paper, we report on research that adapts information navigation based on a user profile structured as a weighted concept hierarchy. A user may create his or her own concept hierarchy and use them for browsing Web sites. Or, the user profile may be created from a reference ontology by 'watching over the user's shoulder' while they browse. We show that these automatically created profiles reflect the user's interests quite well and they are able to produce moderate improvements when applied to search results.
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paper_title: Two-Stage Model for Information Filtering

paper_content:

This thesis presents a novel two-stage model that integrates the theories and techniques from the fields of information retrieval/filtering (IR/IF)and the fields of machine learning and data mining to provide more precise document filtering and retrieval. The first stage is topic filtering. The topic filtering stage is intended to minimize information mismatch by filtering out the most likely irrelevant information based on term-based profiles. Thus, only a relatively small amount of potentially highly relevant documents remain for document ranking. The second stage of the presented method uses pattern mining approach. The objective of the second stage is to solve the problem of information overload. The most likely relevant documents were assigned higher ranks by exploiting patterns in the pattern taxonomy. The second stage is precision oriented. Since relatively small amount of documents are involved at this stage, computational cost is markedly reduced, at the same time, with significant improved results. The new two-stage information filtering model has been evaluated by extensive experiments. The tests were based on well-known IR bench-marking processes, using the latest version of the Reuters dataset, namely Reuters Corpus Volume 1 (RCV1). The performance of the new model was compared with both of the term-based and data mining-based IF models. The results show that more effective and efficient information access has been achieved by combining the strength of information filtering and data mining method.
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paper_title: Domain ontology construction based on semantic relation information of terminology

paper_content:

Ontology is an explicit specification of a conceptualization. That is, ontology is a description of the concepts and relationships that can exist for an agent or a community of agents. This study suggests a method of constructing domain ontology using terminology processing and applies the method to document retrieval. In order to construct ontology, if proposes an algorithm that classifies the patients of nouns and suffices which compose terminology, in domain texts, extracts terminology, and build a hierarchical structure. The experiment used documents related to pharmacy, and the algorithm showed accuracy of 92.57% for singleton terms and 66.64% for multi-word terms on the average. Constructed ontology, which forms natural groups of senses centering on specific nouns or suffices composing the terminology with semantic information, can be utilized in approaching the knowledge of special areas such as document retrieval. According to the result of document retrieval based on the constructed ontology, the system improved in precision and recall compared to traditional keyword-based document retrieval.
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paper_title: Pattern Taxonomy Mining for Information Filtering

paper_content:

This paper examines a new approach to information filtering by using data mining method. This new model consists of two components, namely, topic filtering and pattern taxonomy mining. The aim of using topic filtering is to quickly filter out irrelevant information based on the user profiles. The aim of applying pattern taxonomy mining techniques is to rationalize the data relevance on the reduced data set. Our experiments on Reuters RCV1(Reuters Corpus Volume 1) data collection show that more effective and efficient information access has been achieved by combining the strength of information filtering and data mining method.
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paper_title: Evaluating WordNet-based Measures of Lexical Semantic Relatedness

paper_content:

The quantification of lexical semantic relatedness has many applications in NLP, and many different measures have been proposed. We evaluate five of these measures, all of which use WordNet as their central resource, by comparing their performance in detecting and correcting real-word spelling errors. An information-content-based measure proposed by Jiang and Conrath is found superior to those proposed by Hirst and St-Onge, Leacock and Chodorow, Lin, and Resnik. In addition, we explain why distributional similarity is not an adequate proxy for lexical semantic relatedness.
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paper_title: Reconstructing ddc for interactive classification

paper_content:

The automated text categorization (TC) has made prominent progress in recent years. However, seldom work is done on automatic classification with library classification systems, the largest and most sophisticated classification systems people ever built, such as the Dewey Decimal classification (DDC). The library classification is a very laborious and time-consuming job that requires qualification and good training. The large-scale classification schemes, such as the DDC, impose several obstacles to the state-of-art TC technologies, including very deep hierarchy, data sparseness, and skewed category distribution. These problems characterize large corpora of real-world applications and it is very hard, if not impossible, to obtain satisfactory results. In this paper, we propose a novel algorithm to reconstruct classification schemes according to the document density and category distribution, and to transform the category hierarchy into a balanced virtual taxonomy by merging sparse categories, lopping dense branches and flattening the hierarchy. To make the classification performance acceptable to real-world applications, we also propose an interactive classification model that only needs two or three times of user interaction. Extensive experiments are conducted on a 10-year bibliographic data collection of the Library of Congress to verify the proposed methodology.
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paper_title: An Ontology-Based Framework for Knowledge Retrieval

paper_content:

Retrieving accurate information from the Web is a great challenge to users. The existing information retrieval systems are mostly term-based and thus need to be enhanced toward knowledge-based. User information needs need to be better captured in order to deliver personalized search results. In this paper, an ontology-based framework is proposed for capturing user information needs using a world knowledge base and the user's local instance repository. The framework aims to discover a user's background knowledge for knowledge retrieval. The evaluation result is encouraging, in which the proposed model achieved the same performance as a manual user model.
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paper_title: Automatic Discovery of Part-Whole Relations

paper_content:

An important problem in knowledge discovery from text is the automatic extraction of semantic relations. This paper presents a supervised, semantically intensive, domain independent approach for the automatic detection of part-whole relations in text. First an algorithm is described that identifies lexico-syntactic patterns that encode part-whole relations. A difficulty is that these patterns also encode other semantic relations, and a learning method is necessary to discriminate whether or not a pattern contains a part-whole relation. A large set of training examples have been annotated and fed into a specialized learning system that learns classification rules. The rules are learned through an iterative semantic specialization (ISS) method applied to noun phrase constituents. Classification rules have been generated this way for different patterns such as genitives, noun compounds, and noun phrases containing prepositional phrases to extract part-whole relations from them. The applicability of these rules has been tested on a test corpus obtaining an overall average precision of 80.95% and recall of 75.91%. The results demonstrate the importance of word sense disambiguation for this task. They also demonstrate that different lexico-syntactic patterns encode different semantic information and should be treated separately in the sense that different clarification rules apply to different patterns.
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paper_title: Development of NeuroElectroMagnetic ontologies(NEMO): a framework for mining brainwave ontologies

paper_content:

Event-related potentials (ERP) are brain electrophysiological patterns created by averaging electroencephalographic (EEG) data, time-locking to events of interest (e.g., stimulus or response onset). In this paper, we propose a generic framework for mining anddeveloping domain ontologies and apply it to mine brainwave (ERP) ontologies. The concepts and relationships in ERP ontologies can be mined according to the following steps: pattern decomposition, extraction of summary metrics for concept candidates, hierarchical clustering of patterns for classes and class taxonomies, and clustering-based classification and association rules mining for relationships (axioms) of concepts. We have applied this process to several dense-array (128-channel) ERP datasets. Results suggest good correspondence between mined concepts and rules, on the one hand, and patterns and rules that were independently formulated by domain experts, on the other. Data mining results also suggest ways in which expert-defined rules might be refined to improve ontologyrepresentation and classification results. The next goal of our ERP ontology mining framework is to address some long-standing challenges in conducting large-scale comparison and integration of results across ERP paradigms and laboratories. In a more general context, this work illustrates the promise of an interdisciplinary research program, which combines data mining, neuroinformatics andontology engineering to address real-world problems.
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paper_title: Knowledge discovery using pattern taxonomy model in text mining

paper_content:

In the last decade, many data mining techniques have been proposed for fulfilling various knowledge discovery tasks in order to achieve the goal of retrieving useful information for users. Various types of patterns can then be generated using these techniques, such as sequential patterns, frequent itemsets, and closed and maximum patterns. However, how to effectively exploit the discovered patterns is still an open research issue, especially in the domain of text mining. Most of the text mining methods adopt the keyword-based approach to construct text representations which consist of single words or single terms, whereas other methods have tried to use phrases instead of keywords, based on the hypothesis that the information carried by a phrase is considered more than that by a single term. Nevertheless, these phrase-based methods did not yield significant improvements due to the fact that the patterns with high frequency (normally the shorter patterns) usually have a high value on exhaustivity but a low value on specificity, and thus the specific patterns encounter the low frequency problem. ::: ::: ::: ::: This thesis presents the research on the concept of developing an effective Pattern Taxonomy Model (PTM) to overcome the aforementioned problem by deploying discovered patterns into a hypothesis space. PTM is a pattern-based method which adopts the technique of sequential pattern mining and uses closed patterns as features in the representative. A PTM-based information filtering system is implemented and evaluated by a series of experiments on the latest version of the Reuters dataset, RCV1. The pattern evolution schemes are also proposed in this thesis with the attempt of utilising information from negative training examples to update the discovered knowledge. The results show that the PTM outperforms not only all up-to-date data mining-based methods, but also the traditional Rocchio and the state-of-the-art BM25 and Support Vector Machines (SVM) approaches.
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paper_title: Learning Parts-Based Representations of Data

paper_content:

Many perceptual models and theories hinge on treating objects as a collection of constituent parts. When applying these approaches to data, a fundamental problem arises: how can we determine what are the parts? We attack this problem using learning, proposing a form of generative latent factor model, in which each data dimension is allowed to select a different factor or part as its explanation. This approach permits a range of variations that posit different models for the appearance of a part. Here we provide the details for two such models: a discrete and a continuous one. Further, we show that this latent factor model can be extended hierarchically to account for correlations between the appearances of different parts. This permits modelling of data consisting of multiple categories, and learning these categories simultaneously with the parts when they are unobserved. Experiments demonstrate the ability to learn parts-based representations, and categories, of facial images and user-preference data.
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paper_title: Mining World Knowledge for Analysis of Search Engine Content

paper_content:

Little is known about the content of the major search engines. We present an automatic learning method which trains an ontology with world knowledge of hundreds of different subjects in a three-level taxonomy covering the documents offered in our university library. We then mine this ontology to find important classification rules, and then use these rules to perform an extensive analysis of the content of the largest general purpose internet search engines in use today. Instead of representing documents and collections as a set of terms, we represent them as a set of subjects, which is a highly efficient representation, leading to a more robust representation of information and a decrease of synonymy.
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paper_title: Semantic similarity methods in wordNet and their application to information retrieval on the web

paper_content:

Semantic Similarity relates to computing the similarity between concepts which are not lexicographically similar. We investigate approaches to computing semantic similarity by mapping terms (concepts) to an ontology and by examining their relationships in that ontology. Some of the most popular semantic similarity methods are implemented and evaluated using WordNet as the underlying reference ontology. Building upon the idea of semantic similarity, a novel information retrieval method is also proposed. This method is capable of detecting similarities between documents containing semantically similar but not necessarily lexicographically similar terms. The proposed method has been evaluated in retrieval of images and documents on the Web. The experimental results demonstrated very promising performance improvements over state-of-the-art information retrieval methods.
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paper_title: REPRESENTATION AND CONSTRUCTION OF ONTOLOGIES FOR WEB INTELLIGENCE

paper_content:

Ontologies and agent technology can play a crucial role in enabling Web-based knowledge processing, sharing, and reuse between applications. The paper investigates representation and construction of ontologies for Web intelligence. Three ontology categories are suggested, some of the research and development with respect to the three categories is presented, the major ontology languages are surveyed, and a multi-phase process of automatic construction of the domain-specific ontologies is discussed.
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paper_title: Extracting Hyponyms Of Prespecified Hypernyms From Itemizations And Headings In Web Documents

paper_content:

This paper describes a method to acquire hyponyms for given hypernyms from HTML documents on the WWW. We assume that a heading (or explanation) of an itemization (or listing) in an HTML document is likely to contain a hypernym of the items in the itemization, and we try to acquire hyponymy relations based on this assumption. Our method is obtained by extending Shinzato's method (Shinzato and Torisawa, 2004) where a common hypernym for expressions in itemizations in HTML documents is obtained by using statistical measures. By using Japanese HTML documents, we empirically show that our proposed method can obtain a significant number of hyponymy relations which would otherwise be missed by alternative methods.
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paper_title: Automatising the Learning of Lexical Patterns: an Application to the Enrichment of WordNet by Extracting Semantic Relationships from Wikipedia

paper_content:

This paper describes an automatic approach to identify lexical patterns that represent semantic relationships between concepts in an on-line encyclopedia. Next, these patterns can be applied to extend existing ontologies or semantic networks with new relations. The experiments have been performed with the Simple English Wikipedia and WordNet 1.7. A new algorithm has been devised for automatically generalising the lexical patterns found in the encyclopedia entries. We have found general patterns for the hyperonymy, hyponymy, holonymy and meronymy relations and, using them, we have extracted more than 2600 new relationships that did not appear in WordNet originally. The precision of these relationships depends on the degree of generality chosen for the patterns and the type of relation, being around 60-70% for the best combinations proposed.
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paper_title: Quantify query ambiguity using ODP metadata

paper_content:

Query ambiguity prevents existing retrieval systems from returning reasonable results for every query. As there is already lots of work done on resolving ambiguity, vague queries could be handled using corresponding approaches separately if they can be identified in advance. Quantification of the degree of (lack of) ambiguity laysthe groundwork for the identification. In this poster, we propose such a measure using query topics based on the topic structure selected from the Open Directory Project (ODP) taxonomy. We introduce clarity score to quantify the lack of ambiguity with respect to data sets constructed from the TREC collections and the rank correlation test results demonstrate a strong positive association between the clarity scores and retrieval precisions for queries.
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paper_title: Personalized information recommendation in digital library domain based on ontology

paper_content:

Personalized recommendation is implemented by computing the similarity of user's interests and resources. Most of current recommendation systems compute the similarity based on keywords, which is simply implemented but much semantic information are lost. This paper takes the personalized recommendations of digital library as example, proposes a method to implement personalized recommendation service. User profile and resource features are represented by vector space model. And then these keyword vectors are extended in conceptual level by virtue of domain ontology and HowNet knowledge database. So conceptual space vectors of user and resource are generated. Therefore, personalized recommendation service is provided to user according to the similarity of the conceptual space vectors. Experiment data shows that the similarity based on concept is more efficient than similarity based on keyword in personalized recommendation service.
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paper_title: Using text processing techniques to automatically enrich a domain ontology

paper_content:

Though the utility of domain Ontologies is now widely acknowledgedin an increasing number of domains, several barriers must beovercome before Ontologies become practical and useful tools. Acritical issue is the task of identifying, defining, and enteringthe concept definitions. In case of large and complex applicationdomains this task can be lengthy, costly, and controversial (sincedifferent persons may have different points of view about the sameconcept). To reduce time, cost (and, sometimes, harsh discussions)it is highly advisable to refer, in constructing or updating anontology, to the documents available in the field. In this paper wedescribe OntoLearn, a text-mining tool devised to improvehuman productivity during the process of ontology construction.
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paper_title: Mining ontology for automatically acquiring Web user information needs

paper_content:

It is not easy to obtain the right information from the Web for a particular Web user or a group of users due to the obstacle of automatically acquiring Web user profiles. The current techniques do not provide satisfactory structures for mining Web user profiles. This paper presents a novel approach for this problem. The objective of the approach is to automatically discover ontologies from data sets in order to build complete concept models for Web user information needs. It also proposes a method for capturing evolving patterns to refine discovered ontologies. In addition, the process of assessing relevance in ontology is established. This paper provides both theoretical and experimental evaluations for the approach. The experimental results show that all objectives we expect for the approach are achievable.
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paper_title: Using ODP metadata to personalize search

paper_content:

The Open Directory Project is clearly one of the largest collaborative efforts to manually annotate web pages. This effort involves over 65,000 editors and resulted in metadata specifying topic and importance for more than 4 million web pages. Still, given that this number is just about 0.05 percent of the Web pages indexed by Google, is this effort enough to make a difference? In this paper we discuss how these metadata can be exploited to achieve high quality personalized web search. First, we address this by introducing an additional criterion for web page ranking, namely the distance between a user profile defined using ODP topics and the sets of ODP topics covered by each URL returned in regular web search. We empirically show that this enhancement yields better results than current web search using Google. Then, in the second part of the paper, we investigate the boundaries of biasing PageRank on subtopics of the ODP in order to automatically extend these metadata to the whole web.
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paper_title: An effective approach to document retrieval via utilizing WordNet and recognizing phrases

paper_content:

Noun phrases in queries are identified and classified into four types: proper names, dictionary phrases, simple phrases and complex phrases. A document has a phrase if all content words in the phrase are within a window of a certain size. The window sizes for different types of phrases are different and are determined using a decision tree. Phrases are more important than individual terms. Consequently, documents in response to a query are ranked with matching phrases given a higher priority. We utilize WordNet to disambiguate word senses of query terms. Whenever the sense of a query term is determined, its synonyms, hyponyms, words from its definition and its compound words are considered for possible additions to the query. Experimental results show that our approach yields between 23% and 31% improvements over the best-known results on the TREC 9, 10 and 12 collections for short (title only) queries, without using Web data.
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paper_title: Deploying Approaches for Pattern Refinement in Text Mining

paper_content:

Text mining is the technique that helps users find useful information from a large amount of digital text documents on the Web or databases. Instead of the keyword-based approach which is typically used in this field, the pattern-based model containing frequent sequential patterns is employed to perform the same concept of tasks. However, how to effectively use these discovered patterns is still a big challenge. In this study, we propose two approaches based on the use of pattern deploying strategies. The performance of the pattern deploying algorithms for text mining is investigated on the Reuters dataset RCV1 and the results show that the effectiveness is improved by using our proposed pattern refinement approaches.
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paper_title: Building and Using a Lexical Knowledge Base of Near-Synonym Differences

paper_content:

The initial knowledge base is later enriched with information from other machine-readable dictionaries. Information about the collocational behavior of the near-synonyms is acquired from free text. The knowledge base is used by Xenon, a natural language generation system that shows how the new lexical resource can be used to choose the best near-synonym in specific situations.
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paper_title: Web Mining Research: A Survey

paper_content:

With the huge amount of information available online, the World Wide Web is a fertile area for data mining research. The Web mining research is at the cross road of research from several research communities, such as database, information retrieval, and within AI, especially the sub-areas of machine learning and natural language processing. However, there is a lot of confusions when comparing research efforts from different point of views. In this paper, we survey the research in the area of Web mining, point out some confusions regarded the usage of the term Web mining and suggest three Web mining categories. Then we situate some of the research with respect to these three categories. We also explore the connection between the Web mining categories and the related agent paradigm. For the survey, we focus on representation issues, on the process, on the learning algorithm, and on the application of the recent works as the criteria. We conclude the paper with some research issues.
---
paper_title: Ontology-based personalized search and browsing

paper_content:

As the number of Internet users and the number of accessible Web pages grows, it is becoming increasingly difficult for users to find documents that are relevant to their particular needs. Users must either browse through a large hierarchy of concepts to find the information for which they are looking or submit a query to a publicly available search engine and wade through hundreds of results, most of them irrelevant. The core of the problem is that whether the user is browsing or searching, whether they are an eighth grade student or a Nobel prize winner, the identical information is selected and it is presented the same way. In this paper, we report on research that adapts information navigation based on a user profile structured as a weighted concept hierarchy. A user may create his or her own concept hierarchy and use them for browsing Web sites. Or, the user profile may be created from a reference ontology by 'watching over the user's shoulder' while they browse. We show that these automatically created profiles reflect the user's interests quite well and they are able to produce moderate improvements when applied to search results.
---
paper_title: Two-Stage Model for Information Filtering

paper_content:

This thesis presents a novel two-stage model that integrates the theories and techniques from the fields of information retrieval/filtering (IR/IF)and the fields of machine learning and data mining to provide more precise document filtering and retrieval. The first stage is topic filtering. The topic filtering stage is intended to minimize information mismatch by filtering out the most likely irrelevant information based on term-based profiles. Thus, only a relatively small amount of potentially highly relevant documents remain for document ranking. The second stage of the presented method uses pattern mining approach. The objective of the second stage is to solve the problem of information overload. The most likely relevant documents were assigned higher ranks by exploiting patterns in the pattern taxonomy. The second stage is precision oriented. Since relatively small amount of documents are involved at this stage, computational cost is markedly reduced, at the same time, with significant improved results. The new two-stage information filtering model has been evaluated by extensive experiments. The tests were based on well-known IR bench-marking processes, using the latest version of the Reuters dataset, namely Reuters Corpus Volume 1 (RCV1). The performance of the new model was compared with both of the term-based and data mining-based IF models. The results show that more effective and efficient information access has been achieved by combining the strength of information filtering and data mining method.
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paper_title: Efficient mining of both positive and negative association rules

paper_content:

This paper presents an efficient method for mining both positive and negative association rules in databases. The method extends traditional associations to include association rules of forms A ⇒ ¬ B, ¬ A ⇒ B, and ¬ A ⇒ ¬ B, which indicate negative associations between itemsets. With a pruning strategy and an interestingness measure, our method scales to large databases. The method has been evaluated using both synthetic and real-world databases, and our experimental results demonstrate its effectiveness and efficiency.
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paper_title: Pattern Taxonomy Mining for Information Filtering

paper_content:

This paper examines a new approach to information filtering by using data mining method. This new model consists of two components, namely, topic filtering and pattern taxonomy mining. The aim of using topic filtering is to quickly filter out irrelevant information based on the user profiles. The aim of applying pattern taxonomy mining techniques is to rationalize the data relevance on the reduced data set. Our experiments on Reuters RCV1(Reuters Corpus Volume 1) data collection show that more effective and efficient information access has been achieved by combining the strength of information filtering and data mining method.
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paper_title: Machine learning in automated text categorization

paper_content:

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert labor power, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation.
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paper_title: Exploiting hierarchical relationships in conceptual search

paper_content:

As the number of available Web pages grows, users experience increasing difficulty finding documents relevant to their interests. One of the underlying reasons for this is that most search engines find matches based on keywords, regardless of their meanings. To provide the user with more useful information, we need a system that disambiguates queries by including information about the user's conceptual framework. This is the goal of KeyConcept, a conceptual search engine. During indexing, KeyConcept automatically classifies documents into concepts selected from a reference concept hierarchy. During retrieval, KeyConcept ranks documents based on a combination of keyword and conceptual similarity. This paper describes the system architecture and discusses the results of experiments that evaluate the effect of exploiting the hierarchical relationships between concepts during retrieval. Our results confirm that conceptual match significantly improves the precision of the search results over keyword match alone. In addition, the use of the concept hierarchy to prune irrelevant search results also significantly increases precision.
---
paper_title: Text classification without negative examples revisit

paper_content:

Traditionally, building a classifier requires two sets of examples: positive examples and negative examples. This paper studies the problem of building a text classifier using positive examples (P) and unlabeled examples (U). The unlabeled examples are mixed with both positive and negative examples. Since no negative example is given explicitly, the task of building a reliable text classifier becomes far more challenging. Simply treating all of the unlabeled examples as negative examples and building a classifier thereafter is undoubtedly a poor approach to tackling this problem. Generally speaking, most of the studies solved this problem by a two-step heuristic: first, extract negative examples (N) from U. Second, build a classifier based on P and N. Surprisingly, most studies did not try to extract positive examples from U. Intuitively, enlarging P by P' (positive examples extracted from U) and building a classifier thereafter should enhance the effectiveness of the classifier. Throughout our study, we find that extracting P' is very difficult. A document in U that possesses the features exhibited in P does not necessarily mean that it is a positive example, and vice versa. The very large size of and very high diversity in U also contribute to the difficulties of extracting P'. In this paper, we propose a labeling heuristic called PNLH to tackle this problem. PNLH aims at extracting high quality positive examples and negative examples from U and can be used on top of any existing classifiers. Extensive experiments based on several benchmarks are conducted. The results indicated that PNLH is highly feasible, especially in the situation where |P| is extremely small.
---
paper_title: Interpretations of association rules by granular computing

paper_content:

We present interpretations for association rules. We first introduce Pawlak's method, and the corresponding algorithm of finding decision rules (a kind of association rules). We then use extended random sets to present a new algorithm of finding interesting rules. We prove that the new algorithm is faster than Pawlak's algorithm. The extended random sets are easily to include more than one criterion for determining interesting rules. We also provide two measures for dealing with uncertainties in association rules.
---
paper_title: Granule mining oriented data warehousing model for representations of multidimensional association rules

paper_content:

To promise the quality of multidimensional association mining in real applications is a challenging research issue. The challenging issue is how to represent multidimensional association rules efficiently because of the complicated correlation between attributes. Multi-tier granule mining is one initiative in solving this challenge. This paper presents a granule mining oriented data warehousing model. It can divide attributes into tiers and discover granules for each tier from large multidimensional databases. In addition, it uses association mappings to generate association rules for describing the correlation between tiers. Experiments for the proposed model and the testing results are prosecuted.
---
paper_title: Scalable association-based text classification

paper_content:

Naive Bayes (NB) classifier has long been considered a core methodology in text classification mainly due to its simplicity and computational efficiency. There is an increasing need however for methods that can achieve higher classification accuracy while maintaining the ability to process large document collections. In this paper we examine text categorization methods from a perspective that considers the tradeoff between accuracy and scalability to large data sets and large feature sizes. We start from the observation that Support Vector Machines, one of the best text categorization methods cannot scale up to handle the large document collections involved in many real word problems. We then consider bayesian extensions to NB that achieve higher accuracy by relaxing its strong independence assumptions. Our experimental results show that LB, an association-based lazy classifier can achieve a good tradeoff between high classification accuracy and scalability to large document collections and large feature sizes.
---
paper_title: Development of NeuroElectroMagnetic ontologies(NEMO): a framework for mining brainwave ontologies

paper_content:

Event-related potentials (ERP) are brain electrophysiological patterns created by averaging electroencephalographic (EEG) data, time-locking to events of interest (e.g., stimulus or response onset). In this paper, we propose a generic framework for mining anddeveloping domain ontologies and apply it to mine brainwave (ERP) ontologies. The concepts and relationships in ERP ontologies can be mined according to the following steps: pattern decomposition, extraction of summary metrics for concept candidates, hierarchical clustering of patterns for classes and class taxonomies, and clustering-based classification and association rules mining for relationships (axioms) of concepts. We have applied this process to several dense-array (128-channel) ERP datasets. Results suggest good correspondence between mined concepts and rules, on the one hand, and patterns and rules that were independently formulated by domain experts, on the other. Data mining results also suggest ways in which expert-defined rules might be refined to improve ontologyrepresentation and classification results. The next goal of our ERP ontology mining framework is to address some long-standing challenges in conducting large-scale comparison and integration of results across ERP paradigms and laboratories. In a more general context, this work illustrates the promise of an interdisciplinary research program, which combines data mining, neuroinformatics andontology engineering to address real-world problems.
---
paper_title: Multi-Tier Granule Mining for Representations of Multidimensional Association Rules

paper_content:

It is a big challenge to promise the quality of multidimensional association mining. The essential issue is how to represent meaningful multidimensional association rules efficiently. Currently we have not found satisfactory approaches for solving this challenge because of the complicated correlation between attributes. Multi-tier granule mining is an initiative for solving this challenging issue. It divides attributes into some tiers and then compresses the large multidimensional database into granules at each tier. It also builds association mappings to illustrate the correlation between tiers. In this way, the meaningful association rules can be justified according to these association mappings.
---
paper_title: Text categorization with support vector machines: Learning with many relevant features

paper_content:

This paper explores the use of Support Vector Machines (SVMs) for learning text classifiers from examples. It analyzes the particular properties of learning with text data and identifies why SVMs are appropriate for this task. Empirical results support the theoretical findings. SVMs achieve substantial improvements over the currently best performing methods and behave robustly over a variety of different learning tasks. Furthermore they are fully automatic, eliminating the need for manual parameter tuning.
---
paper_title: Using web structure for classifying and describing web pages

paper_content:

The structure of the web is increasingly being used to improve organization, search, and analysis of information on the web. For example, Google uses the text in citing documents (documents that link to the target document) for search. We analyze the relative utility of document text, and the text in citing documents near the citation, for classification and description. Results show that the text in citing documents, when available, often has greater discriminative and descriptive power than the text in the target document itself. The combination of evidence from a document and citing documents can improve on either information source alone. Moreover, by ranking words and phrases in the citing documents according to expected entropy loss, we are able to accurately name clusters of web pages, even with very few positive examples. Our results confirm, quantify, and extend previous research using web structure in these areas, introducing new methods for classification and description of pages.
---
paper_title: Interest-based personalized search

paper_content:

Web search engines typically provide search results without considering user interests or context. We propose a personalized search approach that can easily extend a conventional search engine on the client side. Our mapping framework automatically maps a set of known user interests onto a group of categories in the Open Directory Project (ODP) and takes advantage of manually edited data available in ODP for training text classifiers that correspond to, and therefore categorize and personalize search results according to user interests. In two sets of controlled experiments, we compare our personalized categorization system (PCAT) with a list interface system (LIST) that mimics a typical search engine and with a nonpersonalized categorization system (CAT). In both experiments, we analyze system performances on the basis of the type of task and query length. We find that PCAT is preferable to LIST for information gathering types of tasks and for searches with short queries, and PCAT outperforms CAT in both information gathering and finding types of tasks, and for searches associated with free-form queries. From the subjects' answers to a questionnaire, we find that PCAT is perceived as a system that can find relevant Web pages quicker and easier than LIST and CAT.
---
paper_title: Building text classifiers using positive and unlabeled examples

paper_content:

We study the problem of building text classifiers using positive and unlabeled examples. The key feature of this problem is that there is no negative example for learning. Recently, a few techniques for solving this problem were proposed in the literature. These techniques are based on the same idea, which builds a classifier in two steps. Each existing technique uses a different method for each step. We first introduce some new methods for the two steps, and perform a comprehensive evaluation of all possible combinations of methods of the two steps. We then propose a more principled approach to solving the problem based on a biased formulation of SVM, and show experimentally that it is more accurate than the existing techniques.
---
paper_title: Mining ontology for automatically acquiring Web user information needs

paper_content:

It is not easy to obtain the right information from the Web for a particular Web user or a group of users due to the obstacle of automatically acquiring Web user profiles. The current techniques do not provide satisfactory structures for mining Web user profiles. This paper presents a novel approach for this problem. The objective of the approach is to automatically discover ontologies from data sets in order to build complete concept models for Web user information needs. It also proposes a method for capturing evolving patterns to refine discovered ontologies. In addition, the process of assessing relevance in ontology is established. This paper provides both theoretical and experimental evaluations for the approach. The experimental results show that all objectives we expect for the approach are achievable.
---
paper_title: Deriving non-redundant approximate association rules from hierarchical datasets

paper_content:

Association rule mining plays an important job in knowledge and information discovery. However, there are still shortcomings with the quality of the discovered rules and often the number of discovered rules is huge and contain redundancies, especially in the case of multi-level datasets. Previous work has shown that the mining of non-redundant rules is a promising approach to solving this problem, with work by [6,8,9,10] focusing on single level datasets. Recent work by Shaw et. al. [7] has extended the non-redundant approaches presented in [6,8,9] to include the elimination of redundant exact basis rules from multi-level datasets. Here we propose a continuation of the work in [7] that allows for the removal of hierarchically redundant approximate basis rules from multi-level datasets by using a dataset's hierarchy or taxonomy.
---
paper_title: Generating concise association rules

paper_content:

Association rule mining has made many achievements in the area of knowledge discovery. However, the quality of the extracted association rules is a big concern. One problem with the quality of the extracted association rules is the huge size of the extracted rule set. As a matter of fact, very often tens of thousands of association rules are extracted among which many are redundant thus useless. Mining non-redundant rules is a promising approach to solve this problem. The Min-max exact basis proposed by Pasquier et al [Pasquier05] has showed exciting results by generating only non-redundant rules. In this paper, we first propose a relaxing definition for redundancy under which the Min-max exact basis still contains redundant rules; then we propose a condensed representation called Reliable exact basis for exact association rules. The rules in the Reliable exact basis are not only non-redundant but also more succinct than the rules in Min-max exact basis. We prove that the redundancy eliminated by the Reliable exact basis does not reduce the belief to the Reliable exact basis. The size of the Reliable exact basis is much smaller than that of the Min-max exact basis. Moreover, we prove that all exact association rules can be deduced from the Reliable exact basis. Therefore the Reliable exact basis is a lossless representation of exact association rules. Experimental results show that the Reliable exact basis significantly reduces the number of non-redundant rules.
---
paper_title: An integrated data preparation scheme for neural network data analysis

paper_content:

Data preparation is an important and critical step in neural network modeling for complex data analysis and it has a huge impact on the success of a wide variety of complex data analysis tasks, such as data mining and knowledge discovery. Although data preparation in neural network data analysis is important, some existing literature about the neural network data preparation are scattered, and there is no systematic study about data preparation for neural network data analysis. In this study, we first propose an integrated data preparation scheme as a systematic study for neural network data analysis. In the integrated scheme, a survey of data preparation, focusing on problems with the data and corresponding processing techniques, is then provided. Meantime, some intelligent data preparation solution to some important issues and dilemmas with the integrated scheme are discussed in detail. Subsequently, a cost-benefit analysis framework for this integrated scheme is presented to analyze the effect of data preparation on complex data analysis. Finally, a typical example of complex data analysis from the financial domain is provided in order to show the application of data preparation techniques and to demonstrate the impact of data preparation on complex data analysis.
---
paper_title: Deploying Approaches for Pattern Refinement in Text Mining

paper_content:

Text mining is the technique that helps users find useful information from a large amount of digital text documents on the Web or databases. Instead of the keyword-based approach which is typically used in this field, the pattern-based model containing frequent sequential patterns is employed to perform the same concept of tasks. However, how to effectively use these discovered patterns is still a big challenge. In this study, we propose two approaches based on the use of pattern deploying strategies. The performance of the pattern deploying algorithms for text mining is investigated on the Reuters dataset RCV1 and the results show that the effectiveness is improved by using our proposed pattern refinement approaches.
---
paper_title: Web Mining Research: A Survey

paper_content:

With the huge amount of information available online, the World Wide Web is a fertile area for data mining research. The Web mining research is at the cross road of research from several research communities, such as database, information retrieval, and within AI, especially the sub-areas of machine learning and natural language processing. However, there is a lot of confusions when comparing research efforts from different point of views. In this paper, we survey the research in the area of Web mining, point out some confusions regarded the usage of the term Web mining and suggest three Web mining categories. Then we situate some of the research with respect to these three categories. We also explore the connection between the Web mining categories and the related agent paradigm. For the survey, we focus on representation issues, on the process, on the learning algorithm, and on the application of the recent works as the criteria. We conclude the paper with some research issues.
---
paper_title: Web search personalization with ontological user profiles

paper_content:

Every user has a distinct background and a specific goal when searching for information on the Web. The goal of Web search personalization is to tailor search results to a particular user based on that user's interests and preferences. Effective personalization of information access involves two important challenges: accurately identifying the user context and organizing the information in such a way that matches the particular context. We present an approach to personalized search that involves building models of user context as ontological profiles by assigning implicitly derived interest scores to existing concepts in a domain ontology. A spreading activation algorithm is used to maintain the interest scores based on the user's ongoing behavior. Our experiments show that re-ranking the search results based on the interest scores and the semantic evidence in an ontological user profile is effective in presenting the most relevant results to the user.
---
paper_title: Ontology-based personalized search and browsing

paper_content:

As the number of Internet users and the number of accessible Web pages grows, it is becoming increasingly difficult for users to find documents that are relevant to their particular needs. Users must either browse through a large hierarchy of concepts to find the information for which they are looking or submit a query to a publicly available search engine and wade through hundreds of results, most of them irrelevant. The core of the problem is that whether the user is browsing or searching, whether they are an eighth grade student or a Nobel prize winner, the identical information is selected and it is presented the same way. In this paper, we report on research that adapts information navigation based on a user profile structured as a weighted concept hierarchy. A user may create his or her own concept hierarchy and use them for browsing Web sites. Or, the user profile may be created from a reference ontology by 'watching over the user's shoulder' while they browse. We show that these automatically created profiles reflect the user's interests quite well and they are able to produce moderate improvements when applied to search results.
---
paper_title: Improving Ontology-Based User Profiles

paper_content:

Personalized Web browsing and search hope to provide Web information that matches a user's personal interests and thus provide more effective and efficient information access. A key feature in developing successful personalized Web applications is to build user profiles that accurately represent a user's interests. The main goal of this research is to investigate techniques that implicitly build ontology-based user profiles. We build the profiles without user interaction, automatically monitoring the user's browsing habits. After building the initial profile from visited Web pages, we investigate techniques to improve the accuracy of the user profile. In particular, we focus on how quickly we can achieve profile stability, how to identify the most important concepts, the effect of depth in the concept-hierarchy on the importance of a concept, and how many levels from the hierarchy should be used to represent the user. ::: ::: Our major findings are that ranking the concepts in the profiles by number of documents assigned to them rather than by accumulated weights provides better profile accuracy. We are also able to identify stable concepts in the profile, thus allowing us to detect long-term user interests. We found that the accuracy of concept detection decreases as we descend them in the concept hierarchy, however this loss of accuracy must be balanced against the detailed view of the user available only through the inclusion of lower-level concepts.
---
paper_title: Ontological user profiling in recommender systems

paper_content:

We explore a novel ontological approach to user profiling within recommender systems, working on the problem of recommending on-line academic research papers. Our two experimental systems, Quickstep and Foxtrot, create user profiles from unobtrusively monitored behaviour and relevance feedback, representing the profiles in terms of a research paper topic ontology. A novel profile visualization approach is taken to acquire profile feedback. Research papers are classified using ontological classes and collaborative recommendation algorithms used to recommend papers seen by similar people on their current topics of interest. Two small-scale experiments, with 24 subjects over 3 months, and a large-scale experiment, with 260 subjects over an academic year, are conducted to evaluate different aspects of our approach. Ontological inference is shown to improve user profiling, external ontological knowledge used to successfully bootstrap a recommender system and profile visualization employed to improve profiling accuracy. The overall performance of our ontological recommender systems are also presented and favourably compared to other systems in the literature.
---
paper_title: Mining ontology for automatically acquiring Web user information needs

paper_content:

It is not easy to obtain the right information from the Web for a particular Web user or a group of users due to the obstacle of automatically acquiring Web user profiles. The current techniques do not provide satisfactory structures for mining Web user profiles. This paper presents a novel approach for this problem. The objective of the approach is to automatically discover ontologies from data sets in order to build complete concept models for Web user information needs. It also proposes a method for capturing evolving patterns to refine discovered ontologies. In addition, the process of assessing relevance in ontology is established. This paper provides both theoretical and experimental evaluations for the approach. The experimental results show that all objectives we expect for the approach are achievable.
---
paper_title: Adaptive web search based on user profile constructed without any effort from users

paper_content:

Web search engines help users find useful information on the World Wide Web (WWW). However, when the same query is submitted by different users, typical search engines return the same result regardless of who submitted the query. Generally, each user has different information needs for his/her query. Therefore, the search result should be adapted to users with different information needs. In this paper, we first propose several approaches to adapting search results according to each user's need for relevant information without any user effort, and then verify the effectiveness of our proposed approaches. Experimental results show that search systems that adapt to each user's preferences can be achieved by constructing user profiles based on modified collaborative filtering with detailed analysis of user's browsing history in one day.
---
paper_title: Web search personalization with ontological user profiles

paper_content:

Every user has a distinct background and a specific goal when searching for information on the Web. The goal of Web search personalization is to tailor search results to a particular user based on that user's interests and preferences. Effective personalization of information access involves two important challenges: accurately identifying the user context and organizing the information in such a way that matches the particular context. We present an approach to personalized search that involves building models of user context as ontological profiles by assigning implicitly derived interest scores to existing concepts in a domain ontology. A spreading activation algorithm is used to maintain the interest scores based on the user's ongoing behavior. Our experiments show that re-ranking the search results based on the interest scores and the semantic evidence in an ontological user profile is effective in presenting the most relevant results to the user.
---
paper_title: Ontology-based personalized search and browsing

paper_content:

As the number of Internet users and the number of accessible Web pages grows, it is becoming increasingly difficult for users to find documents that are relevant to their particular needs. Users must either browse through a large hierarchy of concepts to find the information for which they are looking or submit a query to a publicly available search engine and wade through hundreds of results, most of them irrelevant. The core of the problem is that whether the user is browsing or searching, whether they are an eighth grade student or a Nobel prize winner, the identical information is selected and it is presented the same way. In this paper, we report on research that adapts information navigation based on a user profile structured as a weighted concept hierarchy. A user may create his or her own concept hierarchy and use them for browsing Web sites. Or, the user profile may be created from a reference ontology by 'watching over the user's shoulder' while they browse. We show that these automatically created profiles reflect the user's interests quite well and they are able to produce moderate improvements when applied to search results.
---
paper_title: An Ontology-Based Framework for Knowledge Retrieval

paper_content:

Retrieving accurate information from the Web is a great challenge to users. The existing information retrieval systems are mostly term-based and thus need to be enhanced toward knowledge-based. User information needs need to be better captured in order to deliver personalized search results. In this paper, an ontology-based framework is proposed for capturing user information needs using a world knowledge base and the user's local instance repository. The framework aims to discover a user's background knowledge for knowledge retrieval. The evaluation result is encouraging, in which the proposed model achieved the same performance as a manual user model.
---
paper_title: The TREC 2002 Filtering Track Report

paper_content:

The TREC-10 filtering track measures the ability of systems to build persistent user profiles which successfully separate relevant and non-relevant documents. It consists of three major subtasks: adaptive filtering, batch filtering, and routing. In adaptive filtering, the system begins with only a topic statement and a small number of positive examples, and must learn a better profile from on-line feedback. Batch filtering and routing are more traditional machine learning tasks where the system begins with a large sample of evaluated training documents. This report describes the track, presents some evaluation results, and provides a general commentary on lessons learned from this year's track.
---
paper_title: Probabilistic query expansion using query logs

paper_content:

Query expansion has long been suggested as an effective way to resolve the short query and word mismatching problems. A number of query expansion methods have been proposed in traditional information retrieval. However, these previous methods do not take into account the specific characteristics of web searching; in particular, of the availability of large amount of user interaction information recorded in the web query logs. In this study, we propose a new method for query expansion based on query logs. The central idea is to extract probabilistic correlations between query terms and document terms by analyzing query logs. These correlations are then used to select high-quality expansion terms for new queries. The experimental results show that our log-based probabilistic query expansion method can greatly improve the search performance and has several advantages over other existing methods.
---
paper_title: Improving Ontology-Based User Profiles

paper_content:

Personalized Web browsing and search hope to provide Web information that matches a user's personal interests and thus provide more effective and efficient information access. A key feature in developing successful personalized Web applications is to build user profiles that accurately represent a user's interests. The main goal of this research is to investigate techniques that implicitly build ontology-based user profiles. We build the profiles without user interaction, automatically monitoring the user's browsing habits. After building the initial profile from visited Web pages, we investigate techniques to improve the accuracy of the user profile. In particular, we focus on how quickly we can achieve profile stability, how to identify the most important concepts, the effect of depth in the concept-hierarchy on the importance of a concept, and how many levels from the hierarchy should be used to represent the user. ::: ::: Our major findings are that ranking the concepts in the profiles by number of documents assigned to them rather than by accumulated weights provides better profile accuracy. We are also able to identify stable concepts in the profile, thus allowing us to detect long-term user interests. We found that the accuracy of concept detection decreases as we descend them in the concept hierarchy, however this loss of accuracy must be balanced against the detailed view of the user available only through the inclusion of lower-level concepts.
---
paper_title: Ontological user profiling in recommender systems

paper_content:

We explore a novel ontological approach to user profiling within recommender systems, working on the problem of recommending on-line academic research papers. Our two experimental systems, Quickstep and Foxtrot, create user profiles from unobtrusively monitored behaviour and relevance feedback, representing the profiles in terms of a research paper topic ontology. A novel profile visualization approach is taken to acquire profile feedback. Research papers are classified using ontological classes and collaborative recommendation algorithms used to recommend papers seen by similar people on their current topics of interest. Two small-scale experiments, with 24 subjects over 3 months, and a large-scale experiment, with 260 subjects over an academic year, are conducted to evaluate different aspects of our approach. Ontological inference is shown to improve user profiling, external ontological knowledge used to successfully bootstrap a recommender system and profile visualization employed to improve profiling accuracy. The overall performance of our ontological recommender systems are also presented and favourably compared to other systems in the literature.
---
paper_title: Mining ontology for automatically acquiring Web user information needs

paper_content:

It is not easy to obtain the right information from the Web for a particular Web user or a group of users due to the obstacle of automatically acquiring Web user profiles. The current techniques do not provide satisfactory structures for mining Web user profiles. This paper presents a novel approach for this problem. The objective of the approach is to automatically discover ontologies from data sets in order to build complete concept models for Web user information needs. It also proposes a method for capturing evolving patterns to refine discovered ontologies. In addition, the process of assessing relevance in ontology is established. This paper provides both theoretical and experimental evaluations for the approach. The experimental results show that all objectives we expect for the approach are achievable.
---
paper_title: Web search personalization with ontological user profiles

paper_content:

Every user has a distinct background and a specific goal when searching for information on the Web. The goal of Web search personalization is to tailor search results to a particular user based on that user's interests and preferences. Effective personalization of information access involves two important challenges: accurately identifying the user context and organizing the information in such a way that matches the particular context. We present an approach to personalized search that involves building models of user context as ontological profiles by assigning implicitly derived interest scores to existing concepts in a domain ontology. A spreading activation algorithm is used to maintain the interest scores based on the user's ongoing behavior. Our experiments show that re-ranking the search results based on the interest scores and the semantic evidence in an ontological user profile is effective in presenting the most relevant results to the user.
---
paper_title: Ontology-based personalized search and browsing

paper_content:

As the number of Internet users and the number of accessible Web pages grows, it is becoming increasingly difficult for users to find documents that are relevant to their particular needs. Users must either browse through a large hierarchy of concepts to find the information for which they are looking or submit a query to a publicly available search engine and wade through hundreds of results, most of them irrelevant. The core of the problem is that whether the user is browsing or searching, whether they are an eighth grade student or a Nobel prize winner, the identical information is selected and it is presented the same way. In this paper, we report on research that adapts information navigation based on a user profile structured as a weighted concept hierarchy. A user may create his or her own concept hierarchy and use them for browsing Web sites. Or, the user profile may be created from a reference ontology by 'watching over the user's shoulder' while they browse. We show that these automatically created profiles reflect the user's interests quite well and they are able to produce moderate improvements when applied to search results.
---
paper_title: An Ontology-Based Framework for Knowledge Retrieval

paper_content:

Retrieving accurate information from the Web is a great challenge to users. The existing information retrieval systems are mostly term-based and thus need to be enhanced toward knowledge-based. User information needs need to be better captured in order to deliver personalized search results. In this paper, an ontology-based framework is proposed for capturing user information needs using a world knowledge base and the user's local instance repository. The framework aims to discover a user's background knowledge for knowledge retrieval. The evaluation result is encouraging, in which the proposed model achieved the same performance as a manual user model.
---
paper_title: User profiling

paper_content:

A number of global trends have a large influence on the way we use technology in our life and work, like: increasing connectivity and connected devices (any time, any place, anywhere) broadband increasing data storage capacity, both local and in the network smart objects (ambient intelligence, intelligent multi-modal user interfaces) heterogeneous environments According to technology providers this means enrichment. A necessity for this promise is the adoption of products and services to the profiles of their users. A possible concern for end users is the large number and wide variety of different products and services to deal with. In this workshop we will discuss the width and depth of the topic of profiling and will try to come to requirements and recommendations from an end user perspective.
---
paper_title: The TREC 2002 Filtering Track Report

paper_content:

The TREC-10 filtering track measures the ability of systems to build persistent user profiles which successfully separate relevant and non-relevant documents. It consists of three major subtasks: adaptive filtering, batch filtering, and routing. In adaptive filtering, the system begins with only a topic statement and a small number of positive examples, and must learn a better profile from on-line feedback. Batch filtering and routing are more traditional machine learning tasks where the system begins with a large sample of evaluated training documents. This report describes the track, presents some evaluation results, and provides a general commentary on lessons learned from this year's track.
---
paper_title: Improving Ontology-Based User Profiles

paper_content:

Personalized Web browsing and search hope to provide Web information that matches a user's personal interests and thus provide more effective and efficient information access. A key feature in developing successful personalized Web applications is to build user profiles that accurately represent a user's interests. The main goal of this research is to investigate techniques that implicitly build ontology-based user profiles. We build the profiles without user interaction, automatically monitoring the user's browsing habits. After building the initial profile from visited Web pages, we investigate techniques to improve the accuracy of the user profile. In particular, we focus on how quickly we can achieve profile stability, how to identify the most important concepts, the effect of depth in the concept-hierarchy on the importance of a concept, and how many levels from the hierarchy should be used to represent the user. ::: ::: Our major findings are that ranking the concepts in the profiles by number of documents assigned to them rather than by accumulated weights provides better profile accuracy. We are also able to identify stable concepts in the profile, thus allowing us to detect long-term user interests. We found that the accuracy of concept detection decreases as we descend them in the concept hierarchy, however this loss of accuracy must be balanced against the detailed view of the user available only through the inclusion of lower-level concepts.
---
paper_title: Personalized Web search for improving retrieval effectiveness

paper_content:

Current Web search engines are built to serve all users, independent of the special needs of any individual user. Personalization of Web search is to carry out retrieval for each user incorporating his/her interests. We propose a novel technique to learn user profiles from users' search histories. The user profiles are then used to improve retrieval effectiveness in Web search. A user profile and a general profile are learned from the user's search history and a category hierarchy, respectively. These two profiles are combined to map a user query into a set of categories which represent the user's search intention and serve as a context to disambiguate the words in the user's query. Web search is conducted based on both the user query and the set of categories. Several profile learning and category mapping algorithms and a fusion algorithm are provided and evaluated. Experimental results indicate that our technique to personalize Web search is both effective and efficient.
---
paper_title: Personalizing search via automated analysis of interests and activities

paper_content:

We formulate and study search algorithms that consider a user's prior interactions with a wide variety of content to personalize that user's current Web search. Rather than relying on the unrealistic assumption that people will precisely specify their intent when searching, we pursue techniques that leverage implicit information about the user's interests. This information is used to re-rank Web search results within a relevance feedback framework. We explore rich models of user interests, built from both search-related information, such as previously issued queries and previously visited Web pages, and other information about the user such as documents and email the user has read and created. Our research suggests that rich representations of the user and the corpus are important for personalization, but that it is possible to approximate these representations and provide efficient client-side algorithms for personalizing search. We show that such personalization algorithms can significantly improve on current Web search.
---
paper_title: Ontological user profiling in recommender systems

paper_content:

We explore a novel ontological approach to user profiling within recommender systems, working on the problem of recommending on-line academic research papers. Our two experimental systems, Quickstep and Foxtrot, create user profiles from unobtrusively monitored behaviour and relevance feedback, representing the profiles in terms of a research paper topic ontology. A novel profile visualization approach is taken to acquire profile feedback. Research papers are classified using ontological classes and collaborative recommendation algorithms used to recommend papers seen by similar people on their current topics of interest. Two small-scale experiments, with 24 subjects over 3 months, and a large-scale experiment, with 260 subjects over an academic year, are conducted to evaluate different aspects of our approach. Ontological inference is shown to improve user profiling, external ontological knowledge used to successfully bootstrap a recommender system and profile visualization employed to improve profiling accuracy. The overall performance of our ontological recommender systems are also presented and favourably compared to other systems in the literature.
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paper_title: Mining ontology for automatically acquiring Web user information needs

paper_content:

It is not easy to obtain the right information from the Web for a particular Web user or a group of users due to the obstacle of automatically acquiring Web user profiles. The current techniques do not provide satisfactory structures for mining Web user profiles. This paper presents a novel approach for this problem. The objective of the approach is to automatically discover ontologies from data sets in order to build complete concept models for Web user information needs. It also proposes a method for capturing evolving patterns to refine discovered ontologies. In addition, the process of assessing relevance in ontology is established. This paper provides both theoretical and experimental evaluations for the approach. The experimental results show that all objectives we expect for the approach are achievable.
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paper_title: Toward web intelligence

paper_content:

Web Intelligence (WI) presents excellent opportunities and challenges for the research and development of new generation of Web-based information processing technology, as well as for exploiting Web-based advanced applications. Based on two perspectives of WI research: an intelligent Web-based business-centric schematic diagram and the conceptual levels of WI, we investigates various ways to study WI and potential applications.
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paper_title: Implicit user modeling for personalized search

paper_content:

Information retrieval systems (e.g., web search engines) are critical for overcoming information overload. A major deficiency of existing retrieval systems is that they generally lack user modeling and are not adaptive to individual users, resulting in inherently non-optimal retrieval performance. For example, a tourist and a programmer may use the same word "java" to search for different information, but the current search systems would return the same results. In this paper, we study how to infer a user's interest from the user's search context and use the inferred implicit user model for personalized search. We present a decision theoretic framework and develop techniques for implicit user modeling in information retrieval. We develop an intelligent client-side web search agent (UCAIR) that can perform eager implicit feedback, e.g., query expansion based on previous queries and immediate result reranking based on clickthrough information. Experiments on web search show that our search agent can improve search accuracy over the popular Google search engine.
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paper_title: Personalized query expansion for the web

paper_content:

The inherent ambiguity of short keyword queries demands for enhanced methods for Web retrieval. In this paper we propose to improve such Web queries by expanding them with terms collected from each user's Personal Information Repository, thus implicitly personalizing the search output. We introduce five broad techniques for generating the additional query keywords by analyzing user data at increasing granularity levels, ranging from term and compound level analysis up to global co-occurrence statistics, as well as to using external thesauri. Our extensive empirical analysis under four different scenarios shows some of these approaches to perform very well, especially on ambiguous queries, producing a very strong increase in the quality of the output rankings. Subsequently, we move this personalized search framework one step further and propose to make the expansion process adaptive to various features of each query. A separate set of experiments indicates the adaptive algorithms to bring an additional statistically significant improvement over the best static expansion approach.
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paper_title: Adaptive web search based on user profile constructed without any effort from users

paper_content:

Web search engines help users find useful information on the World Wide Web (WWW). However, when the same query is submitted by different users, typical search engines return the same result regardless of who submitted the query. Generally, each user has different information needs for his/her query. Therefore, the search result should be adapted to users with different information needs. In this paper, we first propose several approaches to adapting search results according to each user's need for relevant information without any user effort, and then verify the effectiveness of our proposed approaches. Experimental results show that search systems that adapt to each user's preferences can be achieved by constructing user profiles based on modified collaborative filtering with detailed analysis of user's browsing history in one day.
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Title: Concept-Based, Personalized Web Information Gathering: A Survey

Section 1: Introduction
Description 1: This section introduces the growing importance of efficient web information gathering and highlights the challenges in the existing systems, setting the stage for the survey.

Section 2: Concept-based Web Information Gathering
Description 2: This section reviews techniques that use semantic concepts for web information gathering, focusing on how these concepts are represented and extracted.

Section 3: Semantic Concept Representation
Description 3: This subsection discusses various methods of representing semantic concepts in the context of web information gathering.

Section 4: Semantic Concept Extraction
Description 4: This subsection covers the techniques used to extract semantic concepts from text documents, including web content mining and text classification.

Section 5: Personalized Web Information Gathering
Description 5: This section presents an overview of personalized web information gathering, emphasizing user profile representation and acquisition.

Section 6: User Profile Representation
Description 6: This subsection explores different ways user profiles are represented in the context of personalized information gathering.

Section 7: User Profile Acquisition
Description 7: This subsection discusses the various methods for acquiring user profiles, including interviewing, non-interviewing, and semi-interviewing techniques.

Section 8: Remarks
Description 8: This section summarizes the survey's findings, mentioning the strengths of concept-based and personalized approaches while noting areas needing improvement.