Finding Social Network for Trust Calculation

1. Finding Social Network for Trust Calculation Yutaka Matsuo, Hironori Tomobe, Koiti Hasida and Mitsuru Ishizuka National Institute of Advance Industrial Science and Technology (AIST) Jemail: y.matsuo@carc.aist.go.jp University of Nagoya, Japan email: tomobe@nagao.nuie.nagoya-u.ac.jp AIST, Japan email: hasida.k@aist.go.jp University of Tokyo, Japan ishizuka@miv.t.u-tokyo.ac.jp ECAI 2004

2. Outline • Abstract • Introduction • Social Network Extraction • Invention of Nodes and Edges • Extraction of Edge Label • Example and Evaluation • Trust Calculation • Social Trust • Individual Trust • Related Works and Conclusion

3. Abstract • Trust is a necessary concept to realize the Semantic Web. • But how can we build a “Web of Trust”? • Small “Web of Trust” => A huge “Web of Trust.” • Focus on an academic community : • as a “microcosm” of a “Web of Trust” • to generate a social network automatically. • Each edge is given a label • Coauthor , Lab , Proj , Conf .

4. Introduction • Based on the trust network, the computer can decide how trustworthy persons, resources, and pieces of information are. • At the beginning : • A person or an organization will trust some acquaintances. • A trust network appears locally and grows gradually by adding new nodes and edges. • According to social scientists : • A person can name 200 to 5000 people • Relations are dynamic • New relations appear every day and old relations weaken gradually.

5. Introduction • Aspects of Knowledge Transfer Structural strong vs. weakties Relational trust Granovetter, 1973 Mayer et al., 1995 Tsai & Ghoshal, 1998 Zaheer et al., 1998 Krackhardt, 1992 Ghoshal et al., 1994 Zand, 1972 Current Study Hansen, 1999 Szulanski, 1996 Nonaka, 1994 Polanyi, 1966 Knowledge tacit vs. explicit Zander & Kogut, 1995

6. Introduction • Berners-Lee : Layer Cake • metadata , ontologies, rules, proofs,

7. Social Network Extraction • An academic society retains member profiles • name, affiliation, qualification,contact address … • Rregular annual conference: • JSAI99, JSAI2000, JSAI2001, and JSAI2002 • 1500 people • Choose 150 members to illustrate network • Edge label : • Coauthor: Coauthors of a technical paper • Lab: Members of the same laboratory or research institute • Proj: Members of the same project or committee • Conf: Participants of the same conference or workshop

8. Social Network Extraction

9. Social Network Extraction • For example • ‘Yutaka Matsuo” (denoted X) • “Hironori Tomobe” (denoted Y) • query “X and Y” to get a documents • query “X or Y” to get b documents • “X and (A or B or . . .)” .. “Y and (A or B or . . .)”

10. Social Network Extraction • Edge Label: • Retrieved by the query “X and Y” and get 3 pages. • First checked 275 pages manually and assigned labels to each page. • manually-selected word groups to characterize pages

11. Social Network Extraction • C4.5

12. Social Network Extraction

13. Trust Calculation • PageRank-like model to measure authoritativeness of each member. • v : member number v = 1509 • n : iterations number set n=1000 • Neighbor(v) : set of nodes each of which is connected to node v • c : constant for normalization • E(v) : uniform over all nodes

14. Trust Calculation

15. Trust Calculation • Individual Trust • n=300 , Vtarget = Yutaka Matsuo

16. Relate Works and Conclusion • First extract a list of members in the community, and try to determine their social network. • Used the contents of the retrieved documents to classify the relation into four categories. • Dan Brickley and Libby Miller invented an RDF vocabulary called FOAF (Friend-of-a-Friend) to create a social network. • In this paper, we argue how local trust networks will finally constitute a huge “Web of Trust.”