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Measuring Accuracy of Triples in Knowledge Graphs

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Language, Data, and Knowledge (LDK 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10318))

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Abstract

An increasing amount of large-scale knowledge graphs have been constructed in recent years. Those graphs are often created from text-based extraction, which could be very noisy. So far, cleaning knowledge graphs are often carried out by human experts and thus very inefficient. It is necessary to explore automatic methods for identifying and eliminating erroneous information. In order to achieve this, previous approaches primarily rely on internal information i.e. the knowledge graph itself. In this paper, we introduce an automatic approach, Triples Accuracy Assessment (TAA), for validating RDF triples (source triples) in a knowledge graph by finding consensus of matched triples (among target triples) from other knowledge graphs. TAA uses knowledge graph interlinks to find identical resources and apply different matching methods between the predicates of source triples and target triples. Then based on the matched triples, TAA calculates a confidence score to indicate the correctness of a source triple. In addition, we present an evaluation of our approach using the FactBench dataset for fact validation. Our findings show promising results for distinguishing between correct and wrong triples.

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Notes

  1. 1.

    dbr refers to http://dbpedia.org/resource, dbo points to http://dbpedia.org/ontology, xsd refers to http://www.w3.org/2001/XMLSchema#.

  2. 2.

    http://wiki.dbpedia.org/.

  3. 3.

    http://yago-knowledge.org/.

  4. 4.

    https://developers.google.com/freebase/.

  5. 5.

    http://www.wikidata.org.

  6. 6.

    sameAs service, http://sameas.org.

  7. 7.

    SameAs4J API, http://99soft.github.io/sameas4j/.

  8. 8.

    owl is a namespace prefix referring to http://www.w3.org/2002/07/owl#.

  9. 9.

    http://www.geonames.org/.

  10. 10.

    http://linkedgeodata.org/.

  11. 11.

    geodata and geonames refer to http://sws.geonames.org/ and http://www.geonames.org/ontology# respectively.

  12. 12.

    https://github.com/SmartDataAnalytics/FactBench.

  13. 13.

    http://iridia.ulb.ac.be/irace/.

  14. 14.

    https://wordnet.princeton.edu/.

  15. 15.

    http://www.nltk.org/.

  16. 16.

    https://github.com/gsi-upm/sematch.

  17. 17.

    https://github.com/TriplesAccuracyAssessment.

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Authors and Affiliations

  1. Knowledge Media Institute, The Open University, Milton Keynes, UK

    Shuangyan Liu, Mathieu d’Aquin & Enrico Motta

Authors
  1. Shuangyan Liu
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  2. Mathieu d’Aquin
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  3. Enrico Motta
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Corresponding author

Correspondence to Shuangyan Liu .

Editor information

Editors and Affiliations

  1. Universidad Politécnica de Madrid, Madrid, Spain

    Jorge Gracia

  2. Nanyang Technological University, Singapore, Singapore

    Francis Bond

  3. Insight Centre for Data Analytics, National University of Ireland, Galway, Galway, Ireland

    John P. McCrae

  4. Insight Centre for Data Analytics, National University of Ireland, Galway, Ireland

    Paul Buitelaar

  5. Goethe-University Frankfurt, Frankfurt, Germany

    Christian Chiarcos

  6. University of Leipzig, Leipzig, Germany

    Sebastian Hellmann

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Liu, S., d’Aquin, M., Motta, E. (2017). Measuring Accuracy of Triples in Knowledge Graphs. In: Gracia, J., Bond, F., McCrae, J., Buitelaar, P., Chiarcos, C., Hellmann, S. (eds) Language, Data, and Knowledge. LDK 2017. Lecture Notes in Computer Science(), vol 10318. Springer, Cham. https://doi.org/10.1007/978-3-319-59888-8_29

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  • DOI: https://doi.org/10.1007/978-3-319-59888-8_29

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