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A Novel Method to Predict Type for DBpedia Entity

  • Thi-Nhu NguyenEmail author
  • Hideaki Takeda
  • Khai Nguyen
  • Ryutaro Ichise
  • Tuan-Dung Cao
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 769)

Abstract

Based on extracting information from Wikipedia, DBpedia is a large scale knowledge base and makes this one available using Semantic Web and Linked Data principles. Thanks to crowd-sourcing, it currently covers multiples domains in multilingualism. Knowledge is obtained from different Wikipedia editions by effort of contributors around the world. Their goal is to manually generate mappings Wikipedia templates into DBpedia ontology classes (types). However, this cause makes the type inconsistency for an entity among different languages. As a result, the quality of data in DBpedia can be affected. In this paper, we present the statement of type consistency for an entity in multilingualism. As a solution for this problem, we propose a method to predict the entity type based on a novel conformity measure. We also evaluate our method based on database extracted from aggregating multilingual resources and compare it with human perception in predicting type for an entity. The experimental result shows that our method can suggest informative types and outperforms the baselines.

Keywords

DBpedia Ontology Mappings Conformity Consistency 

Notes

Acknowledgements

This work was supported by NII (National Institute of Informatics) in Japan based on MOU agreement with Hanoi University of Science and Technology.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Thi-Nhu Nguyen
    • 1
    • 3
    Email author
  • Hideaki Takeda
    • 2
  • Khai Nguyen
    • 2
  • Ryutaro Ichise
    • 2
  • Tuan-Dung Cao
    • 3
  1. 1.Hai Phong UniversityHaiphongVietnam
  2. 2.National Institute of InformaticsTokyoJapan
  3. 3.Hanoi University of Science and TechnologyHanoiVietnam

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