Actively Learning Ontology Matching via User Interaction

1. Actively Learning Ontology Matching via User Interaction Feng Shi, Juanzi Li, Jie Tang, Guotong Xie and Hanyu Li Knowledge Engineering Group Department of Computer Science and Technology Tsinghua University IBM China Research Laboratory, October 27, 2009

2. Outline • Motivation • Problems • Our Approach • Match Selection • Correct Propagation • Experiments • Conclusion

3. Motivation Matching results of the anatomy real world case in OAEI 2009 The results of ontology matching with complete automation is infeasible or undesirable in many real cases. One promising solution is to involve user interactions into the matching process to improve the quality of matching results The users can only give very limited feedback, so is there any way to minimize the amount of user interactions, while maximize the effect of interactive efforts?

4. Agenda • Motivation • Problems • Our Approach • Match Selection • Correct Propagation • Experiments • Conclusion

5. 清华大学知识工程研究室 How to select the most informative candidate match to query? How to improve the whole matching result with the user feedback?

6. Agenda Motivation Problems Our Approach Match Selection Correct Propagation Experiments Conclusion 9/22/2014 6

7. Match Selection • Confidence • Similar Distance If and • Contention Point

8. Motivation

9. Example of the similarity propagation graph

10. Agenda Motivation Core Problems Our Approaches Match Selection Correct Propagation Experiment Results Conclusion 9/22/2014 10

11. if k=2 then n=9

12. Correct Propagation • If the candidate match is unmatched • If the candidate match is confirmed by users

13. 清华大学知识工程研究室

14. Agenda Motivation Problems Our Approach Match Selection Correct Propagation Experiments Conclusion 9/22/2014 14

15. Experiments • Data sets • OAEI 2005 Benchmark Directory • OAEI 2008 Benchmark 301-304 • OAEI 2009 A-R-S Instance Matching Benchmark • Baseline Matching Result • Result of RiMOM • Evaluation Metrics • Precision • Recall • F1-Measure

16. Experiment Design • Exp 1: The effect of the 3 measures • Confidence • Similarity Distance • Contention Point • Exp 2: The effect of the weight for the number of influenced matches • Exp 3: The effect of propagation

17. Exp 1: OAEI 2008 benchmark 302.

18. Exp 2: OAEI 2009 A-R-S Benchmark

19. Exp 3: OAEI 2005 Directory.

20. Agenda Motivation Core Problems Our Approaches Match Selection Correct Propagation Experiment Results Conclusion 9/22/2014 20

21. Conclusion • Propose an active learning framework for ontology matching. • Experiments show that our approach is effective • Batch active learning for ontology matching • Avoid Error feedback from users

22. Thanks!