On the impact of sameAs on schema matching

Joe Raad, Erman Acar, Stefan Schlobach

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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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 specific category of these solutions that 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 impact 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.

Original languageEnglish
Title of host publicationK-CAP '19
Subtitle of host publicationProceedings of the 10th International Conference on Knowledge Capture
PublisherAssociation for Computing Machinery, Inc
Number of pages8
ISBN (Electronic)9781450370080
Publication statusPublished - Sept 2019
Event10th International Conference on Knowledge Capture, K-CAP 2019 - Marina Del Rey, United States
Duration: 19 Nov 201921 Nov 2019


Conference10th International Conference on Knowledge Capture, K-CAP 2019
Country/TerritoryUnited States
CityMarina Del Rey


  • Identity
  • Linked open data
  • Schema matching


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