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1 Combining Provenance with Trust in Social Networks for Semantic Web Content Filtering Jennifer Golbeck University of Maryland, College Park May, 2006.

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Presentation on theme: "1 Combining Provenance with Trust in Social Networks for Semantic Web Content Filtering Jennifer Golbeck University of Maryland, College Park May, 2006."— Presentation transcript:

1 1 Combining Provenance with Trust in Social Networks for Semantic Web Content Filtering Jennifer Golbeck University of Maryland, College Park May, 2006

2 2/12 What are social networks Connections between people Can be –Explicit (people say who they know) –Derived (e.g. from email archives) –Simulated

3 3/12 Web-Based Social Networks (WBSNs) Websites and interfaces that let people maintain browsable lists of friends Last count –142 social networking websites –Over 200,000,000 accounts –Full list at http://trust.mindswap.orghttp://trust.mindswap.org Over 10,000,000 accounts are represented in FOAF, an OWL ontology

4 4/12 Trust in WBSNs People annotate their relationships with information about how much they trust their friends Trust can be binary (trust or don’t trust) or on some scale –This work uses a 1-10 scale where 1 is low trust and 10 is high trust At least 8 social networks have some mechanism for expressing trust

5 5/12 Provenance in Trust Use provenance to mean who made what statement Who said who is trustworthy A E D B C 1 7 9 4 A 1 7 9 4 VS

6 6/12 Inferring Trust The Goal: Select two individuals - the source (node A) and sink (node C) - and recommend to the source how much to trust the sink. ABC t AB t BC t AC

7 7/12 Trust Algorithm If the source does not know the sink, the source asks all of its friends how much to trust the sink, and computes a trust value by a weighted average Neighbors repeat the process if they do not have a direct rating for the sink

8 8/12 Applications of Trust With direct knowledge or a recommendation about how much to trust people, this value can be used as a filter in many applications Since social networks are so prominent on the web, it is a public, accessible data source for determining the quality of annotations and information

9 9/12 Ordering Use trust to determine the order in which information is presented Aggregating If data is aggregated, we can use trust to determine how much weight is given to different sources

10 10/12 Demo FilmTrust http://trust.mindswap.org

11 11/12 Ongoing Work Using trust as priorities in Prioritized Default Logic Taking the provenance of a rule with the trust a user has for the rule’s creator to determine it’s priority First stage of this work to appear at AAAI’06 –(joint work with Yarden Katz)

12 12/12 References Papers and software available at http://trust.mindswap.org http://trust.mindswap.org FilmTrust available at http://trust.mindswap.org/FilmTrust http://trust.mindswap.org/FilmTrust golbeck@cs.umd.edu


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