Trust Networks on the Semantic web Jennifer Golbeck , Bijan Parsia , James Hendler

1. Trust Networks on the Semantic web Jennifer Golbeck, BijanParsia, James Hendler Presented By KaverappaKallangada

2. Topics of discussion • Semantic Web • Trust • Networks on the Semantic Web • Implementation • Applications • Conclusion

3. Working of Semantic Web My agent interacts with another agent to find comedy movies with rating > 8.0 Me: I want to see a movie. Give me some suggestions. Agent: Muppets most wanted has a show in Regal at 6PM. Agent: Captain America and Noah are good options. My agent interacts with regal’s agents and books tickets Me: No, suggest me some comedy movies Me: Ok. Book tickets for the show at 6. My agent interacts with trusted agents to enquire about some good movies

4. Semantic Web Agent: • Collects Web content from diverse sources • Processes information • Exchanges the results with other agents • Intelligent, Autonomous, Reactive, Pro-active Semantic web in real world Linking Open Data cloud diagram ( http://lod-cloud.net/)

5. Structure of Semantic Web

6. Trust • Confidentiality, Authentication and Integrity • Earlier work focused on digital signatures and public key cryptography • Addresses trust as credibility/reliability in a human sense • We can confirm the source of the documents. Can we trust the content? • Gauges the quantitave trust factor. • How much can I trust him on this topic? • My Friend trusts him X units. How much should I trust him?

7. Social Networks • Social network made up of social actors • Small world – Stanley Milgram’s 1960’s work • Two people in the world were separated by only a small number acquaintances • Complex networks share the common features of the small-world phenomenon • small average distance between nodes • high ‘connectance’ • clustering coefficient

8. Networks on the Semantic Web • Set of clustered pages may indicate a common topic • Pages with few outgoing links are less likely to show up. • Hard for a human to see the relationship • Semantic web is machine understandable • Doesn’t need heuristics • Pages are semantically marked • Concepts in pages are automatically linked • Pages and concepts are related across a distributed web • Semantic web is a large graph • Resources => Objects • Predicates => Properties

9. Sub graph allows us to see relations among distributed data • Information about an individual is usually distributed over the network • Digital signatures doesn’t necessarily establish trust • Trust in social network is based on “knows” relation • Person => Node • ‘Knows’ relation => edge • Generates a directed graph • If A doesn’t know B, but some of A’s friend’s know B, then A is ‘close’ to knowing B

10. Trust Network generated for the paper • The edges in a trust network are directed . And weighted. • Infer how much to trust a friend's friend • Estimate the weight of a non existent edge by using the edges in the graph Non existent edge ?? A B C X Y

11. Implementation Base Ontology • Friend of a friend ( FOAF) . • Signed by user => Can be verified • Person identified by email address • Trust can be dependent on person and/or subject areas • Adds properties denoting level of trust on a scale of 1-9 1. Distrusts absolutely 2. Distrusts highly 3. Distrusts moderately 4. Distrusts slightly 5. Trusts neutrally 6. Trusts slightly 7. Trusts moderately 8. Trusts highly 9. Trusts absolutely

12. Examples: <Person rdf:ID="Joe"> <mboxrdf:resource="mailto:bob@example.com"/> <trustsHighlyrdf:resource="#Sue"/> </Person> • A Person having ID as ‘Joe’ personal mail box as ‘bob@example.com’ trustsHighly ‘Sue’ in the topic ‘Ontology Research’ <trustsOnSubjectrdf:resource=http://example.com/ont#Research />

13. Computing Trust • Compute ‘How much’ one should trust another individual • Follows network flow • Maximum and Minimum capacity paths • Maximum amount of trust that the source can give to the sink is min(all_edge_weight_in_the_path) • Users may want to lower trust rating for a user many links away • If A distrusts B regarding a specific topic and B distrusts C on that topic • A might want to rate C a relatively higher • A and B have opposing views and so do B and C. Thus A and C are close to one another. • Or, A may distrust C even more 5 2 7 A B C D For the flow from A to D maximum trust is 2

14. X • If A distrusts B regarding a specific topic and B distrusts C on that topic • For any node with a direct edge to the sink, edge weight is used as the trust value • For the rest, the value is determined by a weighted average of values for each of its neighbors which have a path to the sink • Trust t from node ito node s is given by the function X X A B C 

15. Trust web service • Feed in two email addresses • Weighted average trust value is returned • Java API for querying trust graph • Retrieving a list of neighbors • Trust rating for a given edge • Detecting the presence or absence of paths between two individuals • Finding path length • Trust Allows users to supply their own algorithms for calculating trust. • class file, source and sink email address as input

16. Applications Trust Bot • IRC bot implementing algorithms to make trust recommendation • Builds an internal representation of trust network from a collection of distributed sources • Build happens at runtime and before joining the network • Users can add their URI to the bot • Bot can be queried from an IRC channel for weighted average, maximum and minimum path lengths and maximum and minimum capacity paths

17. Trust Mail • Email client providing inline trust rating for each email • Web service generates trust ratings for TrustMail • Source and sink email address are inputs • Trust rating is a function of topic of email (TrustsRegarding Framework) • If a user has a trust rating with respect to email, that value is used • If there is no specific trust rating, but there is general trust rating, the latter is used • TrustMail lowers the cost of sharing trust judgments

18. Example: Two research groups working on a project together Research Group 1 Research Group 1 PA S1  S2  S3 S2 sending junk email to PB S3 emails S5 S4 sending email to PA PB assigning low trust rating to S2 PB S4 S5  S6 S5 -> PB –> S2 -> S3 S5 -> PB –> PA-> S3

19. Example from the paper

20. Conclusion • Illustrates a method for creating a trust network on semantic web • Web service/ application can be used for calculating trust rating • Non security based efforts can become part of the foundation of the web of trust

21. Future work • Developers should investigate algorithms in-depth for calculating trust • Algorithmic complexity and path length should be considered in trust rating

22. Questions?