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Missing RDF Triples Detection and Correction in Knowledge Graphs

  • Lihua Zhao
  • Rumana Ferdous Munne
  • Natthawut Kertkeidkachorn
  • Ryutaro Ichise
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10675)

Abstract

Knowledge graphs (KGs) have become a powerful asset in information science and technology. To foster enhancing search, information retrieval and question answering domains KGs offer effective structured information. KGs represent real-world entities and their relationships in Resource Description Framework (RDF) triples format. Despite the large amount of knowledge, there are still missing and incorrect knowledge in the KGs. We study the graph patterns of interlinked entities to discover missing and incorrect RDF triples in two KGs - DBpedia and YAGO. We apply graph-based approach to map similar object properties and apply similarity based approach to map similar datatype properties. Our propose methods can utilize those similar ontology properties and efficiently discover missing and incorrect RDF triples in DBpedia and YAGO.

Keywords

RDF triple Knowledge graph Word embedding Ontology matching 

Notes

Acknowledgements

This work was partially supported by NEDO (New Energy and Industrial Technology Development Organization).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Lihua Zhao
    • 1
  • Rumana Ferdous Munne
    • 2
    • 3
  • Natthawut Kertkeidkachorn
    • 2
    • 3
  • Ryutaro Ichise
    • 1
    • 2
    • 3
  1. 1.National Institute of Advanced Industrial Science and TechnologyTokyoJapan
  2. 2.SOKENDAI (The Graduate University for Advanced Studies)HayamaJapan
  3. 3.National Institute of InformaticsTokyoJapan

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