1. Intelligent Database Systems Lab N.Y.U.S.T. I. M. A semantic similarity metric combining features and intrinsic information content Presenter: Chun-Ping Wu Author: Giuseppe Pirro DKE 2009 國立雲林科技大學 National Yunlin University of Science and Technology 2011/01/05

2. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Outline Motivation Objective Methodology Experiments Conclusion Comments 2

3. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Motivation In many research fields, computing semantic similarity between words is an important issue. The previous methods have some drawbacks. 3

4. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Objective To propose a new similarity metric(P&S) to solve the shortcomings of existing approaches.  The P&S metric neither require complex IC computations nor configuration knobs to be adjusted. 4

5. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Information theoretic approaches  Resnik  Lin  J&C 5

6. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Ontology-based approaches  Rada et al.  Hirst and St-Onge 6

7. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Hybrid approaches  Li et al.  OSS 7

8. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology The P&S similarity metric 8

9. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments The P&S similarity experiment 9

10. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments The P&S similarity experiment 10

11. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments The P&S similarity experiment 11

12. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments Evaluation and implementation of the P&S metric 12

13. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments The P&S similarity experiment 13

14. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments Impact of the intrinsic IC formulation 14

15. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments The MeSH ontology 15

16. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Conclusion 16 This paper solves the shortcomings of the previous studies. The P&S metric neither require complex IC computations nor configuration knobs to be adjusted. This metric, as shown by experimental evaluation, outperforms the state of the art.

17. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Comments 17 Advantage  This paper solves the shortcomings of the previous studies.  There are many experiments in this paper. Drawback  It still needs an ontology Application  Semantic similarity, WSD