Detecting Erroneous Identity Links on the Web Using Network Metrics

Raad, Joe; Beek, Wouter; Harmelen, Frank van; Pernelle, Nathalie; Saïs, Fatiha

doi:10.1007/978-3-030-00671-6_23

Cited by 23 publications

(37 citation statements)

References 23 publications

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“…Thus, if two aligned movies violate these patterns, then they are more likely to be wrong. Existing approaches to detect invalid owl:sameAs can be used in this context …”

Section: Problem Statement: Immentioning

confidence: 99%

Data linking over RDF knowledge graphs: A survey

Assi

Mcheick

Dhifli³

2020

Concurrency and Computation

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Summary Instance matching (IM) is the process of matching instances across Knowledge Bases (KBs) that refer to the same real‐world object (eg, the same person in two different KBs). Several approaches in the literature were developed to perform this process using different algorithmic techniques and search strategies. In this article, we aim to provide the rationale for IM and to survey the existing algorithms for performing this task. We begin by identifying the importance of such a process and define it formally. We also provide a new classification of these approaches depending on the “source of evidence,” which can be considered as the context information integrated explicitly or implicitly in the IM process. We survey and discuss the state‐of‐the‐art IM methods regarding the context information. We, furthermore, describe and compare different state‐of‐the‐art IM approaches in relation to several criteria. Such a comprehensive comparative study constitutes an asset and a guide for future research in IM.

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“…Thus, if two aligned movies violate these patterns, then they are more likely to be wrong. Existing approaches to detect invalid owl:sameAs can be used in this context …”

Section: Problem Statement: Immentioning

confidence: 99%

Data linking over RDF knowledge graphs: A survey

Assi

Mcheick

Dhifli³

2020

Concurrency and Computation

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“…This class, which in theory should include instances referring to the same real-world entity, contains in practice around 177K instances referring to various countries, cities, people, and religions. In this context, a recent approach [17,18] tried to limit the eects of such erroneous statements by assigning an error degree to each of these 558M owl:sameAs statements in sameAs.cc, based on the community structure of the owl:sameAs network. These error degrees will be used in this study as indicators of the quality of owl:sameAs links.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, by randomly generating 416K alignments between 833K concepts, we showed that considering all owl:sameAs links increases the Jaccard index of 94 randomly aligned pair of concepts (0.02% of the cases). Following a number of studies showing that owl:sameAs is misused in the Web of data [8,10,17], we investigate in this section whether selecting a subset of these owl:sameAs links, of higher…”

Section: Does the Quality Of Owl:sameas Links Impactmentioning

confidence: 99%

“…With this study, we aim at providing instance-based schema-matching designers with empirical evidences on the benets and drawbacks of using external collections of instance-level interlinks (e.g., from the LOD Cloud) in their tasks. Such study is particularly important, as it follows numerous works showing that a number of these owl:sameAs links are actually erroneous [8,10,17]. This uncertainty regarding the quality of existing owl:sameAs links, along with various other factors such as the way identity, typing and subsumption relations are published in the Web, poses the following two research questions:…”

Section: Introductionmentioning

confidence: 99%

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On the Impact of sameAs on Schema Matching

Raad

Acar

Schlobach

2019

Proceedings of the 10th International Conference on Knowledge Capture

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In a large and decentralised knowledge representation system such as the Web of Data, it is common for data sets to overlap. In the absence of a central naming authority, semantic heterogeneity is inevitable as such overlapping contents are described using different schemas. To overcome this problem, a number of solutions have automated the integration of these data sets by matching their schemas. In this work we focus on a specic category of these solutions, which relies on the concepts' extension for matching the schemas (i.e., instance-based methods). Rather than introducing a new approach for the task of schema matching, this work studies the eect of exploiting the semantics of owl:sameAs in such instance-based methods. For this empirical analysis, we investigate more than 900K concepts extracted from the Web, and make use of over 35B implicit identity assertions to study their impact. The experiments show that despite the growing doubts over their quality, exploiting owl:sameAs assertions extracted from the Web can improve instance-based schema matching techniques.

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“…Dans cet article, qui est une version étendue de (Raad et al, 2018), nous présentons une nouvelle approche pour la détection automatique de liens owl:sameAs potentiellement erronés, qui n'utilise aucune hypothèse sur les données ou sur le schéma. L'approche consiste en l'application d'un algorithme de détection de communautés dans les graphes RDF réduits aux assertions de liens owl:sameAs.…”

Section: Introductionunclassified