A Probabilistic Model for Fine-Grained Expert Search

1. A Probabilistic Model for Fine-Grained Expert Search Shenghua Bao, Huizhong Duan, Qi Zhou, Miao Xiong, Yunbo Cao, Yong Yu June 16--18, 2008, Columbus Ohio

2. Schedule Introduction 1 Fine-grained Expert Search 2 Experimental Results 3 Conclusion 4

3. Search Engine User Query Experts Introduction • Expert Search • “who is an expert on X?” Whoare experts on Semantic Web Search Engine?

4. Introduction • Pioneering Expert Search Systems • Log data in software development • Kautz et al., 1996; Mockus and Herbsleb, 2002; McDonald and Ackerman, 1998; etc. • Email communications • Campbell et al., 2003; Dom et al. 2003; Sihn and Heeren, 2001; etc. • General documents • Yimam, 1996; Davenport and Prusak, 1998; Steer and Lochbaum, 1988; Mattox et al., 1999; Hertzum and Pejtersen, 2000; Craswell et al., 2001; etc.

5. Introduction • Expert Search at TREC • A new task at TREC 2005, 2006, 2007 • Craswell et al., 2005; • Soboroff et al., 2006; • Bailey et al., 2007; • Many approaches have been proposed • Two generative models, Balog et al. 2006 • Prior distribution, relevance feedback, Fang et al. 2006 • Hierarchical language model, Petkova and Croft 2006 • Voting and data fusion, Macdonald and Ounis 2006 • …

6. Different blocks of electronic documents Different functions and qualities Different impacts for expert search Introduction • Coarse-grained approach. • Expert search is carried out under a grain of document. • Further improvements are hard to achieve

7. irrelevant relevant Window queried topic Examples Windowed Section Relation

8. Examples Title Query: Timed Text Author Title-Author Relation 8

9. Examples Reference Section Relation 9

10. Examples Query: W3C Management Team <H1> <H2> Section Title-Body Relation 10

11. Schedule Introduction 1 Fine-grained Expert Search 2 Experimental Results 3 Conclusion 4

12. Fine-grained ExpertSearch --Evidence Extraction <topic, person, relation, document> Fine-grained Evidence • Document-001: “…a high-level plan of the architecture of the semantic web by Tim Berners-Lee… ” “…later, Berners-Lee describes a semantic web search engineexperience…” Whoare experts on Semantic Web Search Engine? Tim Berners-Lee • E1: <semantic web, Tim Berners-Lee, same-section, document-001> • E2: <semantic web search engine, Berners-Lee, same-section, document-001>

13. Fine-grained Expert Search –Search Model Query (q) <topic, person, relation, document> (t,p,r,d) Expert Candidate (c) Expert Matching Model Evidence Matching Model

14. Fine-grained Expert Search -- Expert Matching <topic, person, relation, document> (<t, p, r, d> for short)

15. Fine-grained Expert Search -- Evidence Matching

16. Schedule Introduction 1 Fine-grained Expert Search 2 Experimental Results 3 Conclusion 4

17. Experimental Result • W3C Corpus • 331,307 web pages • 10 training topics of TREC 2005 • 50 test topics of TREC 2005 • 49 test topics of TREC 2006 • Evaluation Metrics • Mean average precision (MAP) • R-precision (R-P) • Top N precision (P@N)

18. Experimental Result • Query Matching

19. Experimental Result • Person Matching

20. Experimental Result • Multiple Relations

21. Experimental Result • Evidence Quality

22. Schedule Introduction 1 Fine-grained Expert Search 2 Experimental Results 3 Conclusion 4

23. Conclusion • Fine-grained expert search • Probabilistic model and its implementation • Evaluation on the TREC data set

24. Thank You !