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DBpedia Entity Type Detection Using Entity Embeddings and N-Gram Models

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Knowledge Engineering and Semantic Web (KESW 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 786))

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Abstract

This paper presents and evaluates a method for the detection of DBpedia entity types (classes) that can be used to assess DBpedia’s quality and to complete missing types for un-typed resources. This method compares entity embeddings with traditional N-gram models coupled with clustering and classification. We evaluate the results for 358 typical DBpedia classes. Our results show that entity embeddings outperform n-gram models for type detection and can contribute to the improvement of DBpedia’s quality, maintenance, and evolution. This is a step toward improving the quality of Linked Open Data in general.

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Notes

  1. 1.

    https://github.com/idio/wiki2vec.

  2. 2.

    https://dumps.wikimedia.org/enwiki/.

  3. 3.

    http://www.nltk.org/.

  4. 4.

    https://lvdmaaten.github.io/tsne/.

  5. 5.

    http://dbpedia.org/page/Barack_Obama.

  6. 6.

    http://weka.sourceforge.net/doc.dev/weka/core/tokenizers/NGramTokenizer.html.

  7. 7.

    http://dbpedia.org/sparql.

  8. 8.

    http://www.site.uottawa.ca/~diana/resources/kesw17/.

  9. 9.

    http://scikit-learn.org/stable/.

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Acknowledgements

We thank the Natural Sciences and Engineering Research Council of Canada (NSERC) for the financial support.

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Authors and Affiliations

  1. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, K1N6N5, Canada

    Hanqing Zhou, Amal Zouaq & Diana Inkpen

Authors
  1. Hanqing Zhou
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  2. Amal Zouaq
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  3. Diana Inkpen
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Correspondence to Hanqing Zhou .

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Editors and Affiliations

  1. West Pomeranian University of Technology in Szczecin, Szczecin, Poland

    Przemysław Różewski

  2. University of Bonn, Bonn, Germany

    Christoph Lange

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Zhou, H., Zouaq, A., Inkpen, D. (2017). DBpedia Entity Type Detection Using Entity Embeddings and N-Gram Models. In: Różewski, P., Lange, C. (eds) Knowledge Engineering and Semantic Web. KESW 2017. Communications in Computer and Information Science, vol 786. Springer, Cham. https://doi.org/10.1007/978-3-319-69548-8_21

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  • DOI: https://doi.org/10.1007/978-3-319-69548-8_21

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