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

  • Hanqing ZhouEmail author
  • Amal Zouaq
  • Diana Inkpen
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 786)

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.

Keywords

Semantic web DBpedia Entity embedding N-Grams Type identification 

Notes

Acknowledgements

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

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada

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