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
Part of the Communications in Computer and Information Science book series (CCIS, volume 480)


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.


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.



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


  1. 1.
    Cruz, I.F., Fabiani, A., Caimi, F., Stroe, C., Palmonari, M.: Automatic configuration selection using ontology matching task profiling. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds.) ESWC 2012. LNCS, vol. 7295, pp. 179–194. Springer, Heidelberg (2012)Google Scholar
  2. 2.
    Kennedy, J., Eberhart, R., et al.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
  3. 3.
    Wang, P., Xu, B.: Lily: ontology alignment results for OAEI 2008. In: Proceedings of the 3rd International Workshop on Ontology Matching, pp. 167–175 (2008)Google Scholar
  4. 4.
    Wang, P.: Lily results on SEALS platform for OAEI. In: Proceedings of the 6th International Workshop on Ontology Matching, pp. 156–162 (2011)Google Scholar
  5. 5.
    Wang, P., Zhou, Y., Xu, B.: Matching large ontologies based on reduction anchors. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI’11), pp. 2343–2348 (2011)Google Scholar
  6. 6.
    Hicks, C.R., Turner, K.V.: Fundamental concepts in the design of experiments (1999)Google Scholar
  7. 7.
    Lee, Y., Sayyadian, M., Doan, A., et al.: eTuner: tuning schema matching software using synthetic scenarios. VLDB J. - Int. J. Very Large Data Bases 16(1), 97–122 (2007)CrossRefGoogle Scholar
  8. 8.
    Duan, S., Thummala, V., Babu, S.: Tuning database configuration parameters with iTuned. Proc. VLDB Endowment 2(1), 1246–1257 (2009)CrossRefGoogle Scholar
  9. 9.
    Thummala, V., Babu, S.: iTuned: a tool for configuring and visualizing database parameters. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, pp. 1231–1234 (2010)Google Scholar
  10. 10.
    Peukert, E., Eberius, J., Rahm, E.: A self-configuring schema matching system. In: 2012 IEEE 28th International Conference on Data Engineering (ICDE), pp. 306–317 (2012)Google Scholar
  11. 11.
    Shvaiko, P., Euzenat, J.: Ten challenges for ontology matching. In: Meersman, R., Tari, Z. (eds.) OTM 2008, Part II. LNCS, vol. 5332, pp. 1164–1182. Springer, Heidelberg (2008)Google Scholar
  12. 12.
    Mochol, M., Jentzsch, A.: Towards a rule-based matcher selection. In: Gangemi, A., Euzenat, J. (eds.) EKAW 2008. LNCS (LNAI), vol. 5268, pp. 109–119. Springer, Heidelberg (2008)Google Scholar
  13. 13.
    Eckert, K., Meilicke, C., Stuckenschmidt, H.: Improving ontology matching using meta-level learning. In: Aroyo, L., Traverso, P., Ciravegna, F., Cimiano, P., Heath, T., Hyvönen, E., Mizoguchi, R., Oren, E., Sabou, M., Simperl, E. (eds.) ESWC 2009. LNCS, vol. 5554, pp. 158–172. Springer, Heidelberg (2009)Google Scholar
  14. 14.
    Zhou, Y.: Extensions of an empirical automated tuning framework. Master Thesis. University of Maryland, College Park (2013)Google Scholar
  15. 15.
    Bock, J., Hettenhausen, J.: Discrete particle swarm optimisation for ontology alignment. Inf. Sci. 192, 152–173 (2012)CrossRefGoogle Scholar

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

Personalised recommendations