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Ontology Matching Tuning Based on Particle Swarm Optimization: Preliminary Results

  • Pan Yang
  • Peng WangEmail author
  • Li Ji
  • Xingyu Chen
  • Kai Huang
  • Bin Yu
Conference paper
  • 625 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 480)

Abstract

An ontology matching system can usually be run with different configurations to optimize the system’s performance, namely precision, recall, or F-measure, depending on the given ontologies to be matched. Changing the configuration has potentially high impact on the obtained matching results. This paper applies particle swarm optimization to automatically tune these configuration parameters through proactively sampling the parameters space and find high-impact parameters and high-performance parameter settings. We show the effectiveness and efficiency of our approach through extensive evaluation on the OAEI 2009 tasks using Lily ontology matching system.

Keywords

Ontology Matching Ontology Alignment Evaluation Initiative (OAEI) High Impact Parameter Automatic Tuning Tuning Task 
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.

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (61472077).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Pan Yang
    • 1
    • 2
  • Peng Wang
    • 1
    • 3
    Email author
  • Li Ji
    • 3
  • Xingyu Chen
    • 3
  • Kai Huang
    • 3
  • Bin Yu
    • 4
  1. 1.School of Computer Science and EngineeringSoutheast UniversityNanjingChina
  2. 2.School of Information Science and EngineeringSoutheast UniversityNanjingChina
  3. 3.College of Software EngineeringSoutheast UniversityNanjingChina
  4. 4.Communication Station of Unit 95028, P.L.A.WuhanChina

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