A survey of current Link Discovery frameworks
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paper_title: Data Linking for the Semantic Web

paper_content:

By specifying that published datasets must link to other existing datasets, the 4th linked data principle ensures a Web of data and not just a set of unconnected data islands. The authors propose in this paper the term data linking to name the problem of finding equivalent resources on the Web of linked data. In order to perform data linking, many techniques were developed, finding their roots in statistics, database, natural language processing and graph theory. The authors begin this paper by providing background information and terminological clarifications related to data linking. Then a comprehensive survey over the various techniques available for data linking is provided. These techniques are classified along the three criteria of granularity, type of evidence, and source of the evidence. Finally, the authors survey eleven recent tools performing data linking and we classify them according to the surveyed techniques.
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paper_title: Question answering on interlinked data

paper_content:

The Data Web contains a wealth of knowledge on a large number of domains. Question answering over interlinked data sources is challenging due to two inherent characteristics. First, different datasets employ heterogeneous schemas and each one may only contain a part of the answer for a certain question. Second, constructing a federated formal query across different datasets requires exploiting links between the different datasets on both the schema and instance levels. We present a question answering system, which transforms user supplied queries (i.e. natural language sentences or keywords) into conjunctive SPARQL queries over a set of interlinked data sources. The contribution of this paper is two-fold: Firstly, we introduce a novel approach for determining the most suitable resources for a user-supplied query from different datasets (disambiguation). We employ a hidden Markov model, whose parameters were bootstrapped with different distribution functions. Secondly, we present a novel method for constructing a federated formal queries using the disambiguated resources and leveraging the linking structure of the underlying datasets. This approach essentially relies on a combination of domain and range inference as well as a link traversal method for constructing a connected graph which ultimately renders a corresponding SPARQL query. The results of our evaluation with three life-science datasets and 25 benchmark queries demonstrate the effectiveness of our approach.
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paper_title: Efficient Multidimensional Blocking for Link Discovery without losing Recall

paper_content:

Over the last three years, an increasing number of data providers have started to publish structured data according to the Linked Data principles on the Web. The resulting Web of Data currently consists of over 28 billion RDF triples. As the Web of Data grows, there is an increasing need for link discovery tools which scale to very large datasets. In record linkage, many partitioning methods have been proposed which substantially reduce the number of required entity comparisons. Unfortunately, most of these methods either lead to a decrease in recall or only work on metric spaces. We propose a novel blocking method called MultiBlock which uses a multidimensional index in which similar objects are located near each other. In each dimension the entities are indexed by a dierent property increasing the efciency of the index signicantly. In addition, it guarantees that no false dismissals can occur. Our approach works on complex link specications which aggregate several dierent similarity measures. MultiBlock has been implemented as part of the Silk Link Discovery Framework. The evaluation shows a speedup factor of several 100 for large datasets compared to the full evaluation without losing recall.
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paper_title: HiBISCuS: Hypergraph-Based Source Selection for SPARQL Endpoint Federation

paper_content:

Efficient federated query processing is of significant importance to tame the large amount of data available on the Web of Data. Previous works have focused on generating optimized query execution plans for fast result retrieval. However, devising source selection approaches beyond triple pattern-wise source selection has not received much attention. This work presents HiBISCuS, a novel hypergraph-based source selection approach to federated SPARQL querying. Our approach can be directly combined with existing SPARQL query federation engines to achieve the same recall while querying fewer data sources. We extend three well-known SPARQL query federation engines with HiBISCus and compare our extensions with the original approaches on FedBench. Our evaluation shows that HiBISCuS can efficiently reduce the total number of sources selected without losing recall. Moreover, our approach significantly reduces the execution time of the selected engines on most of the benchmark queries.
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paper_title: When to Reach for the Cloud: Using Parallel Hardware for Link Discovery

paper_content:

With the ever-growing amount of RDF data available across the Web, the discovery of links between datasets and deduplication of resources within knowledge bases have become tasks of crucial importance. Over the last years, several link discovery approaches have been developed to tackle the runtime and complexity problems that are intrinsic to link discovery. Yet, so far, little attention has been paid to the management of hardware resources for the execution of link discovery tasks. This paper addresses this research gap by investigating the efficient use of hardware resources for link discovery. We implement the \(\mathcal{HR}^3\) approach for three different parallel processing paradigms including the use of GPUs and MapReduce platforms. We also perform a thorough performance comparison for these implementations. Our results show that certain tasks that appear to require cloud computing techniques can actually be accomplished using standard parallel hardware. Moreover, our evaluation provides break-even points that can serve as guidelines for deciding on when to use which hardware for link discovery.
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paper_title: Adoption of the Linked Data Best Practices in Different Topical Domains

paper_content:

The central idea of Linked Data is that data publishers support applications in discovering and integrating data by complying to a set of best practices in the areas of linking, vocabulary usage, and metadata provision. In 2011, the State of the LOD Cloud report analyzed the adoption of these best practices by linked datasets within different topical domains. The report was based on information that was provided by the dataset publishers themselves via the datahub.io Linked Data catalog. In this paper, we revisit and update the findings of the 2011 State of the LOD Cloud report based on a crawl of the Web of Linked Data conducted in April 2014. We analyze how the adoption of the different best practices has changed and present an overview of the linkage relationships between datasets in the form of an updated LOD cloud diagram, this time not based on information from dataset providers, but on data that can actually be retrieved by a Linked Data crawler. Among others, we find that the number of linked datasets has approximately doubled between 2011 and 2014, that there is increased agreement on common vocabularies for describing certain types of entities, and that provenance and license metadata is still rarely provided by the data sources.
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paper_title: Template-based Question Answering over RDF Data

paper_content:

As an increasing amount of RDF data is published as Linked Data, intuitive ways of accessing this data become more and more important. Question answering approaches have been proposed as a good compromise between intuitiveness and expressivity. Most question answering systems translate questions into triples which are matched against the RDF data to retrieve an answer, typically relying on some similarity metric. However, in many cases, triples do not represent a faithful representation of the semantic structure of the natural language question, with the result that more expressive queries can not be answered. To circumvent this problem, we present a novel approach that relies on a parse of the question to produce a SPARQL template that directly mirrors the internal structure of the question. This template is then instantiated using statistical entity identification and predicate detection. We show that this approach is competitive and discuss cases of questions that can be answered with our approach but not with competing approaches.
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paper_title: Linked cancer genome atlas database

paper_content:

The Cancer Genome Atlas (TCGA) is a multidisciplinary, multi-institutional pilot project to create an atlas of genetic mutations responsible for cancer. One of the aims of this project is to develop an infrastructure for making the cancer related data publicly accessible, to enable cancer researchers anywhere around the world to make and validate important discoveries. However, data in the cancer genome atlas are organized as text archives in a set of directories. Devising bioinformatics applications to analyse such data is still challenging, as it requires downloading very large archives and parsing the relevant text files in order to collect the critical co-variates necessary for analysis. Furthermore, the various types of experimental results are not connected biologically, i.e. in order to truly exploit the data in the genome-wide context in which the TCGA project was devised, the data needs to be converted into a structured representation and made publicly available for remote querying and virtual integration. In this work, we address these issues by RDFizing data from TCGA and linking its elements to the Linked Open Data (LOD) Cloud. The outcome is the largest LOD data source (to the best of our knowledge) comprising of over 30 billion triples. This data source can be exploited through publicly available SPARQL endpoints, thus providing an easy-to-use, time-efficient, and scalable solution to accessing the Cancer Genome Atlas. We also describe showcases which are enabled by the new linked data representation of the Cancer Genome Atlas presented in this paper.
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paper_title: Duplicate Record Detection: A Survey

paper_content:

Often, in the real world, entities have two or more representations in databases. Duplicate records do not share a common key and/or they contain errors that make duplicate matching a difficult task. Errors are introduced as the result of transcription errors, incomplete information, lack of standard formats, or any combination of these factors. In this paper, we present a thorough analysis of the literature on duplicate record detection. We cover similarity metrics that are commonly used to detect similar field entries, and we present an extensive set of duplicate detection algorithms that can detect approximately duplicate records in a database. We also cover multiple techniques for improving the efficiency and scalability of approximate duplicate detection algorithms. We conclude with coverage of existing tools and with a brief discussion of the big open problems in the area
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paper_title: Evaluation of entity resolution approaches on real-world match problems

paper_content:

Despite the huge amount of recent research efforts on entity resolution (matching) there has not yet been a comparative evaluation on the relative effectiveness and efficiency of alternate approaches. We therefore present such an evaluation of existing implementations on challenging real-world match tasks. We consider approaches both with and without using machine learning to find suitable parameterization and combination of similarity functions. In addition to approaches from the research community we also consider a state-of-the-art commercial entity resolution implementation. Our results indicate significant quality and efficiency differences between different approaches. We also find that some challenging resolution tasks such as matching product entities from online shops are not sufficiently solved with conventional approaches based on the similarity of attribute values.
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paper_title: Frameworks for entity matching: A comparison

paper_content:

Entity matching is a crucial and difficult task for data integration. Entity matching frameworks provide several methods and their combination to effectively solve different match tasks. In this paper, we comparatively analyze 11 proposed frameworks for entity matching. Our study considers both frameworks which do or do not utilize training data to semi-automatically find an entity matching strategy to solve a given match task. Moreover, we consider support for blocking and the combination of different match algorithms. We further study how the different frameworks have been evaluated. The study aims at exploring the current state of the art in research prototypes of entity matching frameworks and their evaluations. The proposed criteria should be helpful to identify promising framework approaches and enable categorizing and comparatively assessing additional entity matching frameworks and their evaluations.
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paper_title: LogMap: Logic-based and scalable ontology matching

paper_content:

In this paper, we present LogMap--a highly scalable ontology matching system with 'built-in' reasoning and diagnosis capabilities. To the best of our knowledge, LogMap is the only matching system that can deal with semantically rich ontologies containing tens (and even hundreds) of thousands of classes. In contrast to most existing tools, LogMap also implements algorithms for 'on the fly' unsatisfiability detection and repair. Our experiments with the ontologies NCI, FMA and SNOMED CT confirm that our system can efficiently match even the largest existing bio-medical ontologies. Furthermore, LogMap is able to produce a 'clean' set of output mappings in many cases, in the sense that the ontology obtained by integrating LogMap's output mappings with the input ontologies is consistent and does not contain unsatisfiable classes.
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paper_title: Zhishi.links results for OAEI 2011

paper_content:

This report presents the results of Zhishi.links, a distributed instance matching system, for this year's Ontology Alignment Evaluation Initiative (OAEI) campaign. We participate in Data Interlinking track (DI) of IM@OAEI2011. In this report, we briefly describe the architecture and matching strategies of Zhishi.links, followed by an analysis of the results.
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paper_title: When to Reach for the Cloud: Using Parallel Hardware for Link Discovery

paper_content:

With the ever-growing amount of RDF data available across the Web, the discovery of links between datasets and deduplication of resources within knowledge bases have become tasks of crucial importance. Over the last years, several link discovery approaches have been developed to tackle the runtime and complexity problems that are intrinsic to link discovery. Yet, so far, little attention has been paid to the management of hardware resources for the execution of link discovery tasks. This paper addresses this research gap by investigating the efficient use of hardware resources for link discovery. We implement the \(\mathcal{HR}^3\) approach for three different parallel processing paradigms including the use of GPUs and MapReduce platforms. We also perform a thorough performance comparison for these implementations. Our results show that certain tasks that appear to require cloud computing techniques can actually be accomplished using standard parallel hardware. Moreover, our evaluation provides break-even points that can serve as guidelines for deciding on when to use which hardware for link discovery.
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paper_title: Discovering and Maintaining Links on the Web of Data

paper_content:

The Web of Data is built upon two simple ideas: Employ the RDF data model to publish structured data on the Web and to create explicit data links between entities within different data sources. This paper presents the Silk --- Linking Framework, a toolkit for discovering and maintaining data links between Web data sources. Silk consists of three components: 1. A link discovery engine, which computes links between data sources based on a declarative specification of the conditions that entities must fulfill in order to be interlinked; 2. A tool for evaluating the generated data links in order to fine-tune the linking specification; 3. A protocol for maintaining data links between continuously changing data sources. The protocol allows data sources to exchange both linksets as well as detailed change information and enables continuous link recomputation. The interplay of all the components is demonstrated within a life science use case.
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paper_title: A Probabilistic-Logical Framework for Ontology Matching

paper_content:

Ontology matching is the problem of determining correspondences between concepts, properties, and individuals of different heterogeneous ontologies. With this paper we present a novel probabilistic-logical framework for ontology matching based on Markov logic. We define the syntax and semantics and provide a formalization of the ontology matching problem within the framework. The approach has several advantages over existing methods such as ease of experimentation, incoherence mitigation during the alignment process, and the incorporation of a-priori confidence values. We show empirically that the approach is efficient and more accurate than existing matchers on an established ontology alignment benchmark dataset.
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paper_title: SERIMI - Resource Description Similarity, RDF Instance Matching and Interlinking

paper_content:

This paper presents SERIMI, an automatic approach for solving the interlinking problem over RDF data. SERIMI matches instances between a source and a target datasets, without prior knowledge of the data, domain or schema of these datasets. Experiments conducted with benchmark collections demonstrate that our approach considerably outperforms state-of-the-art automatic approaches for solving the interlinking problem over RDF data.
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paper_title: Unsupervised learning of link specifications: deterministic vs. non-deterministic

paper_content:

Link Discovery has been shown to be of utter importance for the Linked Data Web. In previous works, several supervised approaches have been developed for learning link specifications out of labelled data. Most recently, genetic programming has also been utilized to learn link specifications in an unsupervised fashion by optimizing a parametrized pseudo-F-measure. The questions underlying this evaluation paper are twofold: First, how well do pseudo-F-measures predict the real accuracy of non-deterministic and deterministic approaches across different types of datasets? Second, how do deterministic approaches compare to non-deterministic approaches? To answer these questions, we evaluated linear and Boolean classifiers against classifiers computed by using genetic programming on six different data sets. We also studied the correlation between two different pseudo-F-measures and the real F-measures achieved by the classifiers at hand. Our evaluation suggests that pseudo-F-measures behave differently on the synthetic and real data sets.
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paper_title: EAGLE: efficient active learning of link specifications using genetic programming

paper_content:

With the growth of the Linked Data Web, time-efficient approaches for computing links between data sources have become indispensable. Most Link Discovery frameworks implement approaches that require two main computational steps. First, a link specification has to be explicated by the user. Then, this specification must be executed. While several approaches for the time-efficient execution of link specifications have been developed over the last few years, the discovery of accurate link specifications remains a tedious problem. In this paper, we present EAGLE, an active learning approach based on genetic programming. EAGLE generates highly accurate link specifications while reducing the annotation burden for the user. We evaluate EAGLE against batch learning on three different data sets and show that our algorithm can detect specifications with an F-measure superior to 90% while requiring a small number of questions.
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paper_title: Unsupervised learning of link discovery configuration

paper_content:

Discovering links between overlapping datasets on the Web is generally realised through the use of fuzzy similarity measures. Configuring such measures is often a non-trivial task that depends on the domain, ontological schemas, and formatting conventions in data. Existing solutions either rely on the user's knowledge of the data and the domain or on the use of machine learning to discover these parameters based on training data. In this paper, we present a novel approach to tackle the issue of data linking which relies on the unsupervised discovery of the required similarity parameters. Instead of using labeled data, the method takes into account several desired properties which the distribution of output similarity values should satisfy. The method includes these features into a fitness criterion used in a genetic algorithm to establish similarity parameters that maximise the quality of the resulting linkset according to the considered properties. We show in experiments using benchmarks as well as real-world datasets that such an unsupervised method can reach the same levels of performance as manually engineered methods, and how the different parameters of the genetic algorithm and the fitness criterion affect the results for different datasets.
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paper_title: MOMA - A Mapping-based Object Matching System

paper_content:

Object matching or object consolidation is a crucial task for data integration and data cleaning. It addresses the problem of identifying object instances in data sources referring to the same real world entity. We propose a flexible framework called MOMA for mapping-based object matching. It allows the construction of match workflows combining the results of several matcher algorithms on both attribute values and contextual information. The output of a match task is an instance-level mapping that supports information fusion in P2P data integration systems and can be re-used for other match tasks. MOMA utilizes further semantic mappings of different cardinalities and provides merge and compose operators for mapping combination. We propose and evaluate several strategies for both object matching between different sources as well as for duplicate identification within a single data source. 1. MOTIVATION Object matching (also known as object consolidation, duplicate identification, record linkage, entity resolution or reference reconciliation) is a crucial task for data integration and data cleaning [9, 18, 27] and its history goes back over 20 years [3, 19] . It addresses the problem of identifying object instances referring to the same real world entity. The instances may reside in different, typically heterogeneous data sources or may already be stored in a single data source, e.g., in a structured database or a search engine store. The instances to be consolidated may be physically materialized or dynamically be requested from sources, e.g. by database queries or keyword searches. The high importance and difficulty of the object matching problem has triggered a huge amount of research on different variations of the problem. Most previously proposed approaches focus on matching relational records and apply what we call attribute matching, i.e., they use the values of selected attributes to determine the similarity between instances. These approaches have also been called “fuzzy join“ or “fuzzy match“ [6, 7] and considered different similarity functions (e.g., different types of string similarity) [10] and their efficient implementation. More recently, authors have recog
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paper_title: An effective rule miner for instance matching in a web of data

paper_content:

Publishing structured data and linking them to Linking Open Data (LOD) is an ongoing effort to create a Web of data. Each newly involved data source may contain duplicated instances (entities) whose descriptions or schemata differ from those of the existing sources in LOD. To tackle this heterogeneity issue, several matching methods have been developed to link equivalent entities together. Many general-purpose matching methods which focus on similarity metrics suffer from very diverse matching results for different data source pairs. On the other hand, the dataset-specific ones leverage heuristic rules or even manual efforts to ensure the quality, which makes it impossible to apply them to other sources or domains. In this paper, we offer a third choice, a general method of automatically discovering dataset-specific matching rules. In particular, we propose a semi-supervised learning algorithm to iteratively refine matching rules and find new matches of high confidence based on these rules. This dramatically relieves the burden on users of defining rules but still gives high-quality matching results. We carry out experiments on real-world large scale data sources in LOD; the results show the effectiveness of our approach in terms of the precision of discovered matches and the number of missing matches found. Furthermore, we discuss several extensions (like similarity embedded rules, class restriction and SPARQL rewriting) to fit various applications with different requirements.
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paper_title: Parallelizing LIMES for large-scale link discovery

paper_content:

The Linked Open Data cloud consists of more than 26 billion triples, of which less than 3% are links between knowledge bases. However, such links play a central role in key tasks such as cross-ontology question answering, large-scale inferencing and link-based traversal query execution models. The mere size of the Linked Data Cloud makes manual linking impossible. Consequently, Link Discovery Frameworks have been developed over the last years with the aim of providing means to detect links between knowledge bases automatically. Yet, even the current runtime-optimized frameworks for linking lead to unacceptable runtimes when presented with very large datasets. This paper addresses the time complexity of Link Discovery on very large datasets by presenting and evaluating the parallelization of the time-optimized LIMES framework by means of the MapReduce paradigm.
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paper_title: Active learning of expressive linkage rules using genetic programming

paper_content:

A central problem in the context of the Web of Linked Data as well as in data integration in general is to identify entities in different data sources that describe the same real-world object. Many existing methods for matching entities rely on explicit linkage rules, which specify the conditions which must hold true for two entities in order to be interlinked. As writing good linkage rules by hand is a non-trivial problem, the burden to generate links between data sources is still high. In order to reduce the effort and expertise required to write linkage rules, we present the ActiveGenLink algorithm which combines genetic programming and active learning to generate expressive linkage rules interactively. The ActiveGenLink algorithm automates the generation of linkage rules and only requires the user to confirm or decline a number of link candidates. ActiveGenLink uses a query strategy which minimizes user involvement by selecting link candidates which yield a high information gain. Our evaluation shows that ActiveGenLink is capable of generating high quality linkage rules based on labeling a small number of candidate links and that our query strategy for selecting the link candidates outperforms the query-by-vote-entropy baseline.
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paper_title: Interlinking Linked Data Sources Using a Domain-Independent System

paper_content:

Linked data interlinking is the discovery of every owl:sameAs links between given data sources. An owl:sameAs link declares the homogeneous relation between two instances that co-refer to the same real-world object. Traditional methods compare two instances by predefined pairs of RDF predicates, and therefore they rely on the domain of the data. Recently, researchers have attempted to achieve the domain-independent goal by automatically building the linkage rules. However they still require the human curation for the labeled data as the input for learning process. In this paper, we present SLINT+, an interlinking system that is training-free and domain-independent. SLINT+ finds the important predicates of each data sources and combines them to form predicate alignments. The most useful alignments are then selected in the consideration of their confidence. Finally, SLINT+ uses selected predicate alignments as the guide for generating candidate and matching instances. Experimental results show that our system is very efficient when interlinking data sources in 119 different domains. The very considerable improvements on both precision and recall against recent systems are also reported.
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paper_title: Unsupervised Link Discovery through Knowledge Base Repair

paper_content:

The Linked Data Web has developed into a compendium of partly very large datasets. Devising efficient approaches to compute links between these datasets is thus central to achieve the vision behind the Data Web. Unsupervised approaches to achieve this goal have emerged over the last few years. Yet, so far, none of these unsupervised approaches makes use of the replication of resources across several knowledge bases to improve the accuracy it achieves while linking. In this paper, we present Colibri, an iterative unsupervised approach for link discovery. Colibri allows the discovery of links between n datasets (n ≥ 2) while improving the quality of the instance data in these datasets. To this end, Colibri combines error detection and correction with unsupervised link discovery. We evaluate our approach on five benchmark datasets with respect to the F-score it achieves. Our results suggest that Colibri can significantly improve the results of unsupervised machine-learning approaches for link discovery while correctly detecting erroneous resources.
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paper_title: RiMOM2013 results for OAEI 2013

paper_content:

This paper presents the results of RiMOM2013 in the Ontology Alignment Evaluation Initiative (OAEI) 2013. We participated in three tracks of the tasks: Benchmark, IM@OAEI2013, and Multifarm. We first describe the basic framework of our matching System (RiMOM2013); then we describe the alignment process and alignment strategies of RiMOM2013, and then we present specific techniques used for different tracks. At last we give some comments on our results and discuss some future work on RiMOM2013.
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paper_title: Frameworks for entity matching: A comparison

paper_content:

Entity matching is a crucial and difficult task for data integration. Entity matching frameworks provide several methods and their combination to effectively solve different match tasks. In this paper, we comparatively analyze 11 proposed frameworks for entity matching. Our study considers both frameworks which do or do not utilize training data to semi-automatically find an entity matching strategy to solve a given match task. Moreover, we consider support for blocking and the combination of different match algorithms. We further study how the different frameworks have been evaluated. The study aims at exploring the current state of the art in research prototypes of entity matching frameworks and their evaluations. The proposed criteria should be helpful to identify promising framework approaches and enable categorizing and comparatively assessing additional entity matching frameworks and their evaluations.
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paper_title: Efficient Multidimensional Blocking for Link Discovery without losing Recall

paper_content:

Over the last three years, an increasing number of data providers have started to publish structured data according to the Linked Data principles on the Web. The resulting Web of Data currently consists of over 28 billion RDF triples. As the Web of Data grows, there is an increasing need for link discovery tools which scale to very large datasets. In record linkage, many partitioning methods have been proposed which substantially reduce the number of required entity comparisons. Unfortunately, most of these methods either lead to a decrease in recall or only work on metric spaces. We propose a novel blocking method called MultiBlock which uses a multidimensional index in which similar objects are located near each other. In each dimension the entities are indexed by a dierent property increasing the efciency of the index signicantly. In addition, it guarantees that no false dismissals can occur. Our approach works on complex link specications which aggregate several dierent similarity measures. MultiBlock has been implemented as part of the Silk Link Discovery Framework. The evaluation shows a speedup factor of several 100 for large datasets compared to the full evaluation without losing recall.
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paper_title: Dedoop: Efficient Deduplication with Hadoop

paper_content:

We demonstrate a powerful and easy-to-use tool called Dedoop ( De duplication with Ha doop ) for MapReduce-based entity resolution (ER) of large datasets. Dedoop supports a browser-based specification of complex ER workflows including blocking and matching steps as well as the optional use of machine learning for the automatic generation of match classifiers. Specified workflows are automatically translated into MapReduce jobs for parallel execution on different Hadoop clusters. To achieve high performance Dedoop supports several advanced load balancing strategies.
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paper_title: Scaling up all pairs similarity search

paper_content:

Given a large collection of sparse vector data in a high dimensional space, we investigate the problem of finding all pairs of vectors whose similarity score (as determined by a function such as cosine distance) is above a given threshold. We propose a simple algorithm based on novel indexing and optimization strategies that solves this problem without relying on approximation methods or extensive parameter tuning. We show the approach efficiently handles a variety of datasets across a wide setting of similarity thresholds, with large speedups over previous state-of-the-art approaches.
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paper_title: When to Reach for the Cloud: Using Parallel Hardware for Link Discovery

paper_content:

With the ever-growing amount of RDF data available across the Web, the discovery of links between datasets and deduplication of resources within knowledge bases have become tasks of crucial importance. Over the last years, several link discovery approaches have been developed to tackle the runtime and complexity problems that are intrinsic to link discovery. Yet, so far, little attention has been paid to the management of hardware resources for the execution of link discovery tasks. This paper addresses this research gap by investigating the efficient use of hardware resources for link discovery. We implement the \(\mathcal{HR}^3\) approach for three different parallel processing paradigms including the use of GPUs and MapReduce platforms. We also perform a thorough performance comparison for these implementations. Our results show that certain tasks that appear to require cloud computing techniques can actually be accomplished using standard parallel hardware. Moreover, our evaluation provides break-even points that can serve as guidelines for deciding on when to use which hardware for link discovery.
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paper_title: LIMES - A Time-Efficient Approach for Large-Scale Link Discovery on the Web of Data

paper_content:

The Linked Data paradigm has evolved into a powerful enabler for the transition from the document-oriented Web into the Semantic Web. While the amount of data published as Linked Data grows steadily and has surpassed 25 billion triples, less than 5% of these triples are links between knowledge bases. Link discovery frameworks provide the functionality necessary to discover missing links between knowledge bases. Yet, this task requires a significant amount of time, especially when it is carried out on large data sets. This paper presents and evaluates LIMES, a novel time-efficient approach for link discovery in metric spaces. Our approach utilizes the mathematical characteristics of metric spaces during the mapping process to filter out a large number of those instance pairs that do not suffice the mapping conditions. We present the mathematical foundation and the core algorithms employed in LIMES. We evaluate our algorithms with synthetic data to elucidate their behavior on small and large data sets with different configurations and compare the runtime of LIMES with another state-of-the-art link discovery tool.
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paper_title: Link Discovery with Guaranteed Reduction Ratio in Affine Spaces with Minkowski Measures

paper_content:

Time-efficient algorithms are essential to address the complex linking tasks that arise when trying to discover links on the Web of Data. Although several lossless approaches have been developed for this exact purpose, they do not offer theoretical guarantees with respect to their performance. In this paper, we address this drawback by presenting the first Link Discovery approach with theoretical quality guarantees. In particular, we prove that given an achievable reduction ratio r, our Link Discovery approach $\mathcal{HR}^3$ can achieve a reduction ratio r′≤r in a metric space where distances are measured by the means of a Minkowski metric of any order p≥2. We compare $\mathcal{HR}^3$ and the HYPPO algorithm implemented in LIMES 0.5 with respect to the number of comparisons they carry out. In addition, we compare our approach with the algorithms implemented in the state-of-the-art frameworks LIMES 0.5 and SILK 2.5 with respect to runtime. We show that $\mathcal{HR}^3$ outperforms these previous approaches with respect to runtime in each of our four experimental setups.
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paper_title: A survey of approaches to automatic schema matching

paper_content:

Schema matching is a basic problem in many database application domains, such as data integration, E-business, data warehousing, and semantic query processing. In current implementations, schema matching is typically performed manually, which has significant limitations. On the other hand, previous research papers have proposed many techniques to achieve a partial automation of the match operation for specific application domains. We present a taxonomy that covers many of these existing approaches, and we describe the approaches in some detail. In particular, we distinguish between schema-level and instance-level, element-level and structure-level, and language-based and constraint-based matchers. Based on our classification we review some previous match implementations thereby indicating which part of the solution space they cover. We intend our taxonomy and review of past work to be useful when comparing different approaches to schema matching, when developing a new match algorithm, and when implementing a schema matching component.
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paper_title: LogMap: Logic-based and scalable ontology matching

paper_content:

In this paper, we present LogMap--a highly scalable ontology matching system with 'built-in' reasoning and diagnosis capabilities. To the best of our knowledge, LogMap is the only matching system that can deal with semantically rich ontologies containing tens (and even hundreds) of thousands of classes. In contrast to most existing tools, LogMap also implements algorithms for 'on the fly' unsatisfiability detection and repair. Our experiments with the ontologies NCI, FMA and SNOMED CT confirm that our system can efficiently match even the largest existing bio-medical ontologies. Furthermore, LogMap is able to produce a 'clean' set of output mappings in many cases, in the sense that the ontology obtained by integrating LogMap's output mappings with the input ontologies is consistent and does not contain unsatisfiable classes.
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paper_title: String Similarity Metrics for Ontology Alignment

paper_content:

Ontology alignment is an important part of enabling the semantic web to reach its full potential. The vast majority of ontology alignment systems use one or more string similarity metrics, but often the choice of which metrics to use is not given much attention. In this work we evaluate a wide range of such metrics, along with string pre-processing strategies such as removing stop words and considering synonyms, on different types of ontologies. We also present a set of guidelines on when to use which metric. We furthermore show that if optimal string similarity metrics are chosen, those alone can produce alignments that are competitive with the state of the art in ontology alignment systems. Finally, we examine the improvements possible to an existing ontology alignment system using an automated string metric selection strategy based upon the characteristics of the ontologies to be aligned.
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paper_title: LinkLion: A Link Repository for the Web of Data

paper_content:

Links between knowledge bases build the backbone of the Web of Data. Consequently, numerous applications have been developed to compute, evaluate and infer links. Still, the results of many of these applications remain inaccessible to the tools and frameworks that rely upon it. We address this problem by presenting LinkLion, a repository for links between knowledge bases. Our repository is designed as an open-access and open-source portal for the management and distribution of link discovery results. Users are empowered to upload links and specify how these were created. Moreover, users and applications can select and download sets of links via dumps or SPARQL queries. Currently, our portal contains 12.6 million links of 10 different types distributed across 3184 mappings that link 449 datasets. In this demo, we will present the repository as well as different means to access and extend the data it contains. The repository can be found at http://www.linklion.org.
---
paper_title: EAGLE: efficient active learning of link specifications using genetic programming

paper_content:

With the growth of the Linked Data Web, time-efficient approaches for computing links between data sources have become indispensable. Most Link Discovery frameworks implement approaches that require two main computational steps. First, a link specification has to be explicated by the user. Then, this specification must be executed. While several approaches for the time-efficient execution of link specifications have been developed over the last few years, the discovery of accurate link specifications remains a tedious problem. In this paper, we present EAGLE, an active learning approach based on genetic programming. EAGLE generates highly accurate link specifications while reducing the annotation burden for the user. We evaluate EAGLE against batch learning on three different data sets and show that our algorithm can detect specifications with an F-measure superior to 90% while requiring a small number of questions.
---
paper_title: Unsupervised learning of link discovery configuration

paper_content:

Discovering links between overlapping datasets on the Web is generally realised through the use of fuzzy similarity measures. Configuring such measures is often a non-trivial task that depends on the domain, ontological schemas, and formatting conventions in data. Existing solutions either rely on the user's knowledge of the data and the domain or on the use of machine learning to discover these parameters based on training data. In this paper, we present a novel approach to tackle the issue of data linking which relies on the unsupervised discovery of the required similarity parameters. Instead of using labeled data, the method takes into account several desired properties which the distribution of output similarity values should satisfy. The method includes these features into a fitness criterion used in a genetic algorithm to establish similarity parameters that maximise the quality of the resulting linkset according to the considered properties. We show in experiments using benchmarks as well as real-world datasets that such an unsupervised method can reach the same levels of performance as manually engineered methods, and how the different parameters of the genetic algorithm and the fitness criterion affect the results for different datasets.
---
paper_title: LogMap and LogMapLt results for OAEI 2013

paper_content:

We present the results obtained by our ontology matching system LogMap and its 'lightweight' variant called LogMapLt within the OAEI 2012 campaign. The LogMap project started in January 2011 with the objective of developing a scalable and logic-based ontology matching system. This is our third participation in the OAEI and the experience has so far been very positive.
---
paper_title: EAGLE: efficient active learning of link specifications using genetic programming

paper_content:

With the growth of the Linked Data Web, time-efficient approaches for computing links between data sources have become indispensable. Most Link Discovery frameworks implement approaches that require two main computational steps. First, a link specification has to be explicated by the user. Then, this specification must be executed. While several approaches for the time-efficient execution of link specifications have been developed over the last few years, the discovery of accurate link specifications remains a tedious problem. In this paper, we present EAGLE, an active learning approach based on genetic programming. EAGLE generates highly accurate link specifications while reducing the annotation burden for the user. We evaluate EAGLE against batch learning on three different data sets and show that our algorithm can detect specifications with an F-measure superior to 90% while requiring a small number of questions.
---
paper_title: DEQA: deep web extraction for question answering

paper_content:

Despite decades of effort, intelligent object search remains elusive. Neither search engine nor semantic web technologies alone have managed to provide usable systems for simple questions such as "find me a flat with a garden and more than two bedrooms near a supermarket." ::: ::: We introduce deqa, a conceptual framework that achieves this elusive goal through combining state-of-the-art semantic technologies with effective data extraction. To that end, we apply deqa, to the UK real estate domain and show that it can answer a significant percentage of such questions correctly. deqa achieves this by mapping natural language questions to Sparql patterns. These patterns are then evaluated on an RDF database of current real estate offers. The offers are obtained using OXPath, a state-of-the-art data extraction system, on the major agencies in the Oxford area and linked through Limes to background knowledge such as the location of supermarkets.
---
paper_title: SERIMI results for OAEI 2011

paper_content:

This paper presents the results of SERIMI in the Ontology Alignment Evaluation Initiative (OAEI) 2011. We participate in the track [email protected] (IMEI) of the campaign. We first describe the basic interlinking process and interlinking strategies in SERIMI, and then we present specific techniques used in this track. We conclude with a discussion of our results, and possible directions to improve SERIMI in future work.
---
paper_title: String Similarity Metrics for Ontology Alignment

paper_content:

Ontology alignment is an important part of enabling the semantic web to reach its full potential. The vast majority of ontology alignment systems use one or more string similarity metrics, but often the choice of which metrics to use is not given much attention. In this work we evaluate a wide range of such metrics, along with string pre-processing strategies such as removing stop words and considering synonyms, on different types of ontologies. We also present a set of guidelines on when to use which metric. We furthermore show that if optimal string similarity metrics are chosen, those alone can produce alignments that are competitive with the state of the art in ontology alignment systems. Finally, we examine the improvements possible to an existing ontology alignment system using an automated string metric selection strategy based upon the characteristics of the ontologies to be aligned.
---
paper_title: Zhishi.links results for OAEI 2011

paper_content:

This report presents the results of Zhishi.links, a distributed instance matching system, for this year's Ontology Alignment Evaluation Initiative (OAEI) campaign. We participate in Data Interlinking track (DI) of IM@OAEI2011. In this report, we briefly describe the architecture and matching strategies of Zhishi.links, followed by an analysis of the results.
---
paper_title: An effective rule miner for instance matching in a web of data

paper_content:

Publishing structured data and linking them to Linking Open Data (LOD) is an ongoing effort to create a Web of data. Each newly involved data source may contain duplicated instances (entities) whose descriptions or schemata differ from those of the existing sources in LOD. To tackle this heterogeneity issue, several matching methods have been developed to link equivalent entities together. Many general-purpose matching methods which focus on similarity metrics suffer from very diverse matching results for different data source pairs. On the other hand, the dataset-specific ones leverage heuristic rules or even manual efforts to ensure the quality, which makes it impossible to apply them to other sources or domains. In this paper, we offer a third choice, a general method of automatically discovering dataset-specific matching rules. In particular, we propose a semi-supervised learning algorithm to iteratively refine matching rules and find new matches of high confidence based on these rules. This dramatically relieves the burden on users of defining rules but still gives high-quality matching results. We carry out experiments on real-world large scale data sources in LOD; the results show the effectiveness of our approach in terms of the precision of discovered matches and the number of missing matches found. Furthermore, we discuss several extensions (like similarity embedded rules, class restriction and SPARQL rewriting) to fit various applications with different requirements.
---
paper_title: A String Metric for Ontology Alignment

paper_content:

Ontologies are today a key part of every knowledge based system. They provide a source of shared and precisely defined terms, resulting in system interoperability by knowledge sharing and reuse. Unfortunately, the variety of ways that a domain can be conceptualized results in the creation of different ontologies with contradicting or overlapping parts. For this reason ontologies need to be brought into mutual agreement (aligned). One important method for ontology alignment is the comparison of class and property names of ontologies using string-distance metrics. Today quite a lot of such metrics exist in literature. But all of them have been initially developed for different applications and fields, resulting in poor performance when applied in this new domain. In the current paper we present a new string metric for the comparison of names which performs better on the process of ontology alignment as well as to many other field matching problems.
---
paper_title: I.: Large-scale interactive ontology matching: Algorithms and implementation

paper_content:

In this paper we present the ontology matching system LogMap 2, a much improved version of its predecessor LogMap. LogMap 2 supports user interaction during the matching process, which is essential for use cases requiring very accurate mappings. Interactivity, however, imposes very strict scalability requirements; we are able to satisfy these requirements by providing real-time user response even for large-scale ontologies. Finally, LogMap 2 implements scalable reasoning and diagnosis algorithms, which minimise any logical inconsistencies introduced by the matching process.
---
paper_title: CODI: Combinatorial Optimization for Data Integration: results for OAEI 2011

paper_content:

In this paper, we describe our probabilistic-logical alignment system CODI (Combinatorial Optimization for Data Integration). The system provides a declarative framework for the alignment of individuals, concepts, and properties of two heterogeneous ontologies. CODI leverages both logical schema information and lexical similarity measures with a well-defined semantics for A-Box and T-Box matching. The alignments are computed by solving corresponding combinatorial optimization problems.
---
paper_title: Zhishi.links results for OAEI 2011

paper_content:

This report presents the results of Zhishi.links, a distributed instance matching system, for this year's Ontology Alignment Evaluation Initiative (OAEI) campaign. We participate in Data Interlinking track (DI) of IM@OAEI2011. In this report, we briefly describe the architecture and matching strategies of Zhishi.links, followed by an analysis of the results.
---
paper_title: An effective rule miner for instance matching in a web of data

paper_content:

Publishing structured data and linking them to Linking Open Data (LOD) is an ongoing effort to create a Web of data. Each newly involved data source may contain duplicated instances (entities) whose descriptions or schemata differ from those of the existing sources in LOD. To tackle this heterogeneity issue, several matching methods have been developed to link equivalent entities together. Many general-purpose matching methods which focus on similarity metrics suffer from very diverse matching results for different data source pairs. On the other hand, the dataset-specific ones leverage heuristic rules or even manual efforts to ensure the quality, which makes it impossible to apply them to other sources or domains. In this paper, we offer a third choice, a general method of automatically discovering dataset-specific matching rules. In particular, we propose a semi-supervised learning algorithm to iteratively refine matching rules and find new matches of high confidence based on these rules. This dramatically relieves the burden on users of defining rules but still gives high-quality matching results. We carry out experiments on real-world large scale data sources in LOD; the results show the effectiveness of our approach in terms of the precision of discovered matches and the number of missing matches found. Furthermore, we discuss several extensions (like similarity embedded rules, class restriction and SPARQL rewriting) to fit various applications with different requirements.
---
paper_title: When to Reach for the Cloud: Using Parallel Hardware for Link Discovery

paper_content:

With the ever-growing amount of RDF data available across the Web, the discovery of links between datasets and deduplication of resources within knowledge bases have become tasks of crucial importance. Over the last years, several link discovery approaches have been developed to tackle the runtime and complexity problems that are intrinsic to link discovery. Yet, so far, little attention has been paid to the management of hardware resources for the execution of link discovery tasks. This paper addresses this research gap by investigating the efficient use of hardware resources for link discovery. We implement the \(\mathcal{HR}^3\) approach for three different parallel processing paradigms including the use of GPUs and MapReduce platforms. We also perform a thorough performance comparison for these implementations. Our results show that certain tasks that appear to require cloud computing techniques can actually be accomplished using standard parallel hardware. Moreover, our evaluation provides break-even points that can serve as guidelines for deciding on when to use which hardware for link discovery.
---
paper_title: Parallelizing LIMES for large-scale link discovery

paper_content:

The Linked Open Data cloud consists of more than 26 billion triples, of which less than 3% are links between knowledge bases. However, such links play a central role in key tasks such as cross-ontology question answering, large-scale inferencing and link-based traversal query execution models. The mere size of the Linked Data Cloud makes manual linking impossible. Consequently, Link Discovery Frameworks have been developed over the last years with the aim of providing means to detect links between knowledge bases automatically. Yet, even the current runtime-optimized frameworks for linking lead to unacceptable runtimes when presented with very large datasets. This paper addresses the time complexity of Link Discovery on very large datasets by presenting and evaluating the parallelization of the time-optimized LIMES framework by means of the MapReduce paradigm.
---
paper_title: Scaling up all pairs similarity search

paper_content:

Given a large collection of sparse vector data in a high dimensional space, we investigate the problem of finding all pairs of vectors whose similarity score (as determined by a function such as cosine distance) is above a given threshold. We propose a simple algorithm based on novel indexing and optimization strategies that solves this problem without relying on approximation methods or extensive parameter tuning. We show the approach efficiently handles a variety of datasets across a wide setting of similarity thresholds, with large speedups over previous state-of-the-art approaches.
---
paper_title: Frameworks for entity matching: A comparison

paper_content:

Entity matching is a crucial and difficult task for data integration. Entity matching frameworks provide several methods and their combination to effectively solve different match tasks. In this paper, we comparatively analyze 11 proposed frameworks for entity matching. Our study considers both frameworks which do or do not utilize training data to semi-automatically find an entity matching strategy to solve a given match task. Moreover, we consider support for blocking and the combination of different match algorithms. We further study how the different frameworks have been evaluated. The study aims at exploring the current state of the art in research prototypes of entity matching frameworks and their evaluations. The proposed criteria should be helpful to identify promising framework approaches and enable categorizing and comparatively assessing additional entity matching frameworks and their evaluations.
---
paper_title: SEALS Methodology for Evaluation Campaigns

paper_content:

This document describes the second version of the SEALS methodology for evaluation campaigns that is being used in the organization of the different SEALS Evaluation Campaigns.
---
paper_title: EAGLE: efficient active learning of link specifications using genetic programming

paper_content:

With the growth of the Linked Data Web, time-efficient approaches for computing links between data sources have become indispensable. Most Link Discovery frameworks implement approaches that require two main computational steps. First, a link specification has to be explicated by the user. Then, this specification must be executed. While several approaches for the time-efficient execution of link specifications have been developed over the last few years, the discovery of accurate link specifications remains a tedious problem. In this paper, we present EAGLE, an active learning approach based on genetic programming. EAGLE generates highly accurate link specifications while reducing the annotation burden for the user. We evaluate EAGLE against batch learning on three different data sets and show that our algorithm can detect specifications with an F-measure superior to 90% while requiring a small number of questions.
---
paper_title: Unsupervised learning of link discovery configuration

paper_content:

Discovering links between overlapping datasets on the Web is generally realised through the use of fuzzy similarity measures. Configuring such measures is often a non-trivial task that depends on the domain, ontological schemas, and formatting conventions in data. Existing solutions either rely on the user's knowledge of the data and the domain or on the use of machine learning to discover these parameters based on training data. In this paper, we present a novel approach to tackle the issue of data linking which relies on the unsupervised discovery of the required similarity parameters. Instead of using labeled data, the method takes into account several desired properties which the distribution of output similarity values should satisfy. The method includes these features into a fitness criterion used in a genetic algorithm to establish similarity parameters that maximise the quality of the resulting linkset according to the considered properties. We show in experiments using benchmarks as well as real-world datasets that such an unsupervised method can reach the same levels of performance as manually engineered methods, and how the different parameters of the genetic algorithm and the fitness criterion affect the results for different datasets.
---
paper_title: An effective rule miner for instance matching in a web of data

paper_content:

Publishing structured data and linking them to Linking Open Data (LOD) is an ongoing effort to create a Web of data. Each newly involved data source may contain duplicated instances (entities) whose descriptions or schemata differ from those of the existing sources in LOD. To tackle this heterogeneity issue, several matching methods have been developed to link equivalent entities together. Many general-purpose matching methods which focus on similarity metrics suffer from very diverse matching results for different data source pairs. On the other hand, the dataset-specific ones leverage heuristic rules or even manual efforts to ensure the quality, which makes it impossible to apply them to other sources or domains. In this paper, we offer a third choice, a general method of automatically discovering dataset-specific matching rules. In particular, we propose a semi-supervised learning algorithm to iteratively refine matching rules and find new matches of high confidence based on these rules. This dramatically relieves the burden on users of defining rules but still gives high-quality matching results. We carry out experiments on real-world large scale data sources in LOD; the results show the effectiveness of our approach in terms of the precision of discovered matches and the number of missing matches found. Furthermore, we discuss several extensions (like similarity embedded rules, class restriction and SPARQL rewriting) to fit various applications with different requirements.
---
paper_title: LIMES - A Time-Efficient Approach for Large-Scale Link Discovery on the Web of Data

paper_content:

The Linked Data paradigm has evolved into a powerful enabler for the transition from the document-oriented Web into the Semantic Web. While the amount of data published as Linked Data grows steadily and has surpassed 25 billion triples, less than 5% of these triples are links between knowledge bases. Link discovery frameworks provide the functionality necessary to discover missing links between knowledge bases. Yet, this task requires a significant amount of time, especially when it is carried out on large data sets. This paper presents and evaluates LIMES, a novel time-efficient approach for link discovery in metric spaces. Our approach utilizes the mathematical characteristics of metric spaces during the mapping process to filter out a large number of those instance pairs that do not suffice the mapping conditions. We present the mathematical foundation and the core algorithms employed in LIMES. We evaluate our algorithms with synthetic data to elucidate their behavior on small and large data sets with different configurations and compare the runtime of LIMES with another state-of-the-art link discovery tool.
---
paper_title: Active learning of expressive linkage rules using genetic programming

paper_content:

A central problem in the context of the Web of Linked Data as well as in data integration in general is to identify entities in different data sources that describe the same real-world object. Many existing methods for matching entities rely on explicit linkage rules, which specify the conditions which must hold true for two entities in order to be interlinked. As writing good linkage rules by hand is a non-trivial problem, the burden to generate links between data sources is still high. In order to reduce the effort and expertise required to write linkage rules, we present the ActiveGenLink algorithm which combines genetic programming and active learning to generate expressive linkage rules interactively. The ActiveGenLink algorithm automates the generation of linkage rules and only requires the user to confirm or decline a number of link candidates. ActiveGenLink uses a query strategy which minimizes user involvement by selecting link candidates which yield a high information gain. Our evaluation shows that ActiveGenLink is capable of generating high quality linkage rules based on labeling a small number of candidate links and that our query strategy for selecting the link candidates outperforms the query-by-vote-entropy baseline.
---
paper_title: Evaluation of entity resolution approaches on real-world match problems

paper_content:

Despite the huge amount of recent research efforts on entity resolution (matching) there has not yet been a comparative evaluation on the relative effectiveness and efficiency of alternate approaches. We therefore present such an evaluation of existing implementations on challenging real-world match tasks. We consider approaches both with and without using machine learning to find suitable parameterization and combination of similarity functions. In addition to approaches from the research community we also consider a state-of-the-art commercial entity resolution implementation. Our results indicate significant quality and efficiency differences between different approaches. We also find that some challenging resolution tasks such as matching product entities from online shops are not sufficiently solved with conventional approaches based on the similarity of attribute values.
---
paper_title: Interlinking Linked Data Sources Using a Domain-Independent System

paper_content:

Linked data interlinking is the discovery of every owl:sameAs links between given data sources. An owl:sameAs link declares the homogeneous relation between two instances that co-refer to the same real-world object. Traditional methods compare two instances by predefined pairs of RDF predicates, and therefore they rely on the domain of the data. Recently, researchers have attempted to achieve the domain-independent goal by automatically building the linkage rules. However they still require the human curation for the labeled data as the input for learning process. In this paper, we present SLINT+, an interlinking system that is training-free and domain-independent. SLINT+ finds the important predicates of each data sources and combines them to form predicate alignments. The most useful alignments are then selected in the consideration of their confidence. Finally, SLINT+ uses selected predicate alignments as the guide for generating candidate and matching instances. Experimental results show that our system is very efficient when interlinking data sources in 119 different domains. The very considerable improvements on both precision and recall against recent systems are also reported.
---
paper_title: Silk - A Link Discovery Framework for the Web of Data

paper_content:

Web of Data is built upon two simple ideas: Employ the RDF data model to publish structured data on the Web and to set explicit RDF links between entities within different data sources. This paper presents the Silk - Link Discovery Framework, a tool for finding relationships between entities within different data sources. Data publishers can use Silk to set RDF links from their data sources to other data sources on the Web. Silk features a declarative language for specifying which types of RDF links should be discovered between data sources as well as which conditions entities must fulfill in order to be interlinked. Link conditions may be based on various similarity metrics and can take the graph around entities into account, which is addressed using a path-based selector language. Silk accesses data sources over the SPARQL protocol and can thus be used without having to replicate datasets locally.
---
paper_title: String Similarity Metrics for Ontology Alignment

paper_content:

Ontology alignment is an important part of enabling the semantic web to reach its full potential. The vast majority of ontology alignment systems use one or more string similarity metrics, but often the choice of which metrics to use is not given much attention. In this work we evaluate a wide range of such metrics, along with string pre-processing strategies such as removing stop words and considering synonyms, on different types of ontologies. We also present a set of guidelines on when to use which metric. We furthermore show that if optimal string similarity metrics are chosen, those alone can produce alignments that are competitive with the state of the art in ontology alignment systems. Finally, we examine the improvements possible to an existing ontology alignment system using an automated string metric selection strategy based upon the characteristics of the ontologies to be aligned.
---
paper_title: Evaluation of entity resolution approaches on real-world match problems

paper_content:

Despite the huge amount of recent research efforts on entity resolution (matching) there has not yet been a comparative evaluation on the relative effectiveness and efficiency of alternate approaches. We therefore present such an evaluation of existing implementations on challenging real-world match tasks. We consider approaches both with and without using machine learning to find suitable parameterization and combination of similarity functions. In addition to approaches from the research community we also consider a state-of-the-art commercial entity resolution implementation. Our results indicate significant quality and efficiency differences between different approaches. We also find that some challenging resolution tasks such as matching product entities from online shops are not sufficiently solved with conventional approaches based on the similarity of attribute values.
---
paper_title: Pushing the Limits of Instance Matching Systems: A Semantics-Aware Benchmark for Linked Data

paper_content:

The architectural choices behind the Data Web have led to the publication of large interrelated data sets that contain different descriptions for the same real-world objects. Due to the mere size of current online datasets, such duplicate instances are most commonly detected (semi-)automatically using instance matching frameworks. Choosing the right framework for this purpose remains tedious, as current instance matching benchmarks fail to provide end users and developers with the necessary insights pertaining to how current frameworks behave when dealing with real data. In this poster, we present the Semantic Publishing Instance Matching Benchmark (SPIMBENCH) which allows the benchmarking of instance matching systems against not only structure-based and value-based test cases, but also against semantics-aware test cases based on OWL axioms. SPIMBENCH features a scalable data generator and a weighted gold standard that can be used for debugging instance matching systems and for reporting how well they perform in various matching tasks.
---

Title: A Survey of Current Link Discovery Frameworks

Section 1: Introduction
Description 1: Provide background information and motivation for studying link discovery frameworks, including the importance of linking datasets in the LOD cloud and the challenges involved in the manual linking process.

Section 2: Link Discovery Problem
Description 2: Describe the link discovery problem, including the basic definition, challenges, and differences between link discovery and entity resolution.

Section 3: Requirements
Description 3: Discuss the main requirements for a link discovery framework, such as effectiveness, efficiency, low configuration effort, support for online link discovery, and the provision of a powerful infrastructure.

Section 4: LD Workflow
Description 4: Present the generic workflow commonly applied by current link discovery frameworks, outlining the pre-processing, matching, and post-processing phases.

Section 5: Configuration
Description 5: Detail the configuration phase of the link discovery process, including the specification of similarity measures, the role of manually and automatically specified configurations, and learning-based approaches.

Section 6: Runtime Optimization
Description 6: Explain the techniques for runtime optimization, such as blocking and filtering, to reduce the search space and improve efficiency during the matching phase.

Section 7: Match Approaches
Description 7: Outline the different match approaches used in link discovery, including element-level and structure-based matchers, as well as the use of background knowledge.

Section 8: Postprocessing
Description 8: Describe the post-processing phase, focusing on the selection, verification, and refinement of computed links to ensure consistency and quality.

Section 9: Functional Comparison
Description 9: Provide a functional comparison of various link discovery frameworks based on the requirements and workflow discussed previously.

Section 10: Data Input
Description 10: Discuss the data input capabilities of the frameworks, such as support for different file formats and dynamic data access via SPARQL endpoints.

Section 11: Matching Strategies
Description 11: Compare the matching strategies supported by different frameworks, including built-in similarity measures and structural matching techniques.

Section 12: Support for Parallel LD
Description 12: Evaluate the support for parallel link discovery in the frameworks, including the use of MapReduce and GPU-based processing.

Section 13: User Interface and Interaction
Description 13: Assess the user interface and interaction capabilities, including the availability of graphical user interfaces and APIs for external access.

Section 14: Availability for Other Researchers
Description 14: Discuss the availability of the frameworks for other researchers, including open-source status and access to software.

Section 15: Summarizing Observations
Description 15: Summarize the observations from the functional comparison, discussing how the frameworks meet the posed requirements and areas for future improvement.

Section 16: Comparison of Evaluation Results
Description 16: Analyze the published evaluation results of the frameworks, with special emphasis on results from the Ontology Alignment Evaluation Initiative (OAEI) instance matching track.

Section 17: Evaluation Results of OAEI Tasks
Description 17: Present detailed results for specific OAEI instance matching tasks, comparing the performance of different frameworks.

Section 18: Other Evaluations
Description 18: Discuss additional evaluation results beyond the OAEI tasks, including scalability and efficiency evaluations on various datasets.

Section 19: Observations and Outlook
Description 19: Provide observations on the current state of link discovery framework evaluations and propose future directions for improving comparative evaluations.

Section 20: Conclusion
Description 20: Summarize the findings from the survey, highlighting the strengths and weaknesses of the frameworks and providing final recommendations for future research and development.