1. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Named Entity Disambiguation by Leveraging Wikipedia Semantic Knowledge Presenter : Jiang-Shan Wang Authors : Xianpei Han, Jun Zhao CIKM 2009 國立雲林科技大學 National Yunlin University of Science and Technology 1

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

3. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Motivation The traditional bag of words (BOW) based methods ignores:  Semantic relations  Associative relatedness  Polysemy  Synonymy The social network based methods:  Only capture a special type of semantic relations  Limited coverage 3

4. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Objective 4 To proposes a disambiguation method using Wikipedia as background knowledge.

5. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Method Wikipedia semantic knowledge  Wikipedia concepts  Surface forms of Wikipedia concepts  Semantic relations between Wikipedia concepts  Semantic relatedness between Wikipedia concepts Named entity disambiguation  Representing name observation  Measuring the similarity between name observations  Grouping name observations 5

6. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Method Wikipedia concepts Surface forms of Wikipedia concepts 6

7. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Method Semantic relations between Wikipedia concepts Semantic relatedness 7

8. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Method Representing name observation  Surface form identification  Mapping surface forms to concepts  Concepts weight and pruning 8

9. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Method Measuring the similarity 9

10. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Method Measuring the similarity 10

11. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Method Grouping name observations 11

12. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiment 12

13. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiment 13

14. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiment WePS1 WePS2 14

15. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Conclusion 15 This method can achieve appealing results:  It get 10.7% improvement over the traditional BOW based method  It get 16.7% improvement over the traditional social network based method

16. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Comments 16 Advantage  Good semantic knowledge base. Drawback Application  Information retrieval  Disambiguation