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: [email protected]
University of Nagoya, Japan email: [email protected], Japan email: [email protected]
University of Tokyo, Japan [email protected]
ECAI 2004
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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
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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 .
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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.
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Introduction• Aspects of Knowledge Transfer
CurrentStudy
Structuralstrong vs. weak ties
Relationaltrust
Knowledgetacit vs. explicit
Hansen, 1999
Tsai & Ghoshal, 1998
Mayer et al., 1995
Zand, 1972
Zaheer et al., 1998
Nonaka, 1994
Polanyi, 1966
Zander & Kogut, 1995
Szulanski,1996
Krackhardt, 1992
Ghoshal et al., 1994
Granovetter, 1973
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Introduction
• Berners-Lee : Layer Cake• metadata , ontologies, rules, proofs,
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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
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Social Network Extraction
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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 . . .)”
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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
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Social Network Extraction
• C4.5
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Social Network Extraction
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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
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Trust Calculation
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Trust Calculation• Individual Trust
• n=300 , Vtarget = Yutaka Matsuo
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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.”
