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Learning Disjointness Axioms With Association Rule Mining and Its Application to Inconsistency Detection of Linked Data

  • Yanfang MaEmail author
  • Huan Gao
  • Tianxing Wu
  • Guilin Qi
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 480)

Abstract

Disjointness between two concepts is useful to discover new information and detect inconsistencies in knowledge bases. Association rule mining, as a way to discover implicit knowledge in massive data, has been applied to learn disjointness axioms. In this paper, we first analyse the existing method to learn disjointness axioms using association rule mining. Based on the analysis, we propose an improvement of association rule mining for learning disjointness axioms. We then apply the learned disjointness axioms to inconsistency detection in DBpedia and Zhishi.me.

Keywords

Learning Disjointness Association Rule Mining Disjointness Axioms Inconsistency Detection Negative Association Rules 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringSoutheast UniversityNanjingChina

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