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An Ensemble Matchers Based Rank Aggregation Method for Taxonomy Matching

  • Hailun LinEmail author
  • Yuanzhuo Wang
  • Yantao Jia
  • Jinhua Xiong
  • Peng Zhang
  • Xueqi Cheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9313)

Abstract

Taxonomy matching is an important operation of knowledge base merging. Several matchers for automating taxonomy matching have been proposed and evaluated in the knowledge base community. Studies reveal that there is no single taxonomy matcher suitable for any domain-specific taxonomy mapping, therefore an ensemble of taxonomy matchers is essential. In this paper, we propose taxonomy metamatching, a distributed computing framework for assembling taxonomy matchers and generating an optimal taxonomy mapping. And we introduce TRA, a Threshold Rank Aggregation algorithm for this problem. Experimental results show that TRA outperforms state-of-the-art approaches regardless of domains and scales of taxonomies, which demonstrates that TRA performs good adaptability to taxonomy matching.

Keywords

knowledge base merging taxonomy matching rank aggregation 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Hailun Lin
    • 1
    Email author
  • Yuanzhuo Wang
    • 1
  • Yantao Jia
    • 1
  • Jinhua Xiong
    • 1
  • Peng Zhang
    • 2
  • Xueqi Cheng
    • 1
  1. 1.Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  2. 2.Institute of Information EngineeringChinese Academy of SciencesBeijingChina

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