trust on the semantic web seyyed asgary ghasempouri sharif university of technology computer...
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Trust on the Semantic Web
Seyyed asgary ghasempouri
Sharif University of TechnologyComputer Department
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Outline
Web of Trust? Objective of paper & Contributions Networks in Semantic Web? How to build a Trust Network? Trust Graph Computation of Trust Trust Web Service Applications -> TrustBot, TrustMail Related Works Conclusion
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Web of Trust? Web of trust-> each user explicitly specify a
(possibly small) set of users she trusts. The resulting web of trust may be used recursively to compute a user’s trust in any other user
Web of trust Research has been concentrated more on source of
information which misses trust in terms of human sense. Focused largely on digital signatures, certificates,
authentications.
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Contributions Apply social networks to semantic web Consider trust in to account with a much more
human sense. Ex: How much credence should I give to a what this
person says about a topic The degree of trust associated with it could be based on
your past encounter or could be based on what your friends says about him
Build a Trust Network extending FOAF ontology & by adding their own Trust Ontology
Compute trust values between two people Illustrate its usefulness using applications
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Networks on Semantic Web Information is machine readable Concepts in semantically marked up pages are
automatically linked through ontological relations visualized as a large graph where web resources are
nodes & edges form relations between objects or webpages
Generating Social Networks Individuals manage data about themselves and
their friends Information about individuals in a network is
maintained in distributed sources Digital signature can be associated to files going
across the network Security measures builds trust about the
authenticity or data contained within the network
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Building Trust Network FOAF can be used to describe information
about himself, such as name, email address, homepage, people he knows
Extended FOAF ontology (Friend-Of-A-Friend) Following properties were added to it, which allows users to
indicate a level of trust for people they know Trust properties
Trusts neutrally, Trusts slightly, Trusts moderately, Trusts highly, Trusts absolutely
Distrust properties Distrust absolutely, Distrust highly, Distrust moderately,
Distrust slightly
Users can sign these files so that information source can be verified
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Example 1<Person rdf:ID="Joe">
<mbox rdf:resource="mailto:[email protected]"/>
<trustsHighly rdf:resource="#Sue"/>
</Person>
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Example 2<Person rdf:ID="Bob"><mbox rdf:resource="mailto:[email protected]"/><trustsHighlyRe>
<TrustsRegarding><trustsPerson rdf:resource="#Dan"/><trustsOnSubjectrdf:resource="http://example.com/ont#Research"/>
</TrustsRegarding></trustsHighlyRe><distrustsAbsolutelyRe>
<TrustsRegarding><trustsPerson rdf:resource="#Dan"/><trustsOnSubject
rdf:resource="http://example.com/ont#AutoRepair"/></TrustsRegarding>
</distrustsAbsolutelyRe></Person>
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Trust Graph Directed Edges in the graph contain explicitly
specified trust values It can be used to infer the trust values between two
people who are not directly connected Several Basic calculations
Maximum and minimum capacity paths Identify the trust capacity of the paths with highest
lowest respectively Determined by making a network flow calculation for
each individual path between the source and sink Maximum amount of trust a source can give to a
sink is limited by the smallest edge weight along the path
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Trust Graph Contd..
Maximum and minimum length paths measure of the number of edges between the source
and the sink Weighted average between two people (node X & Y)
General notion is that users would want lower trust ratings for someone many links away as opposed to a direct neighbor
Distrust notion is very ambiguous: Ex: A distrust B regarding a specific subject and in turn, B
distrust C on that subject, it is possible that A distrust C, or A trust C.
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Trust Calculation It uses the maximum
capacity of each path to the sink
Algorithm is recursive & calculates the average
For any node that has direct edge to sink node , they ignore the paths & use the direct edge weight.
Otherwise they determine the weighted average values for each of the neighbors, which have a path to sink
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Trust Web Service
Trust Web service A web users can provide two email addresses &
in return the service would return the weighted average
User can provide their own algorithms for calculating trust
It retrieves the neighbors, gets the list of trust rating for a given edge, detecting the presence or absence of path between two individuals, & finding path lengths.
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Applications -TrustBot
TrustBot is an IRC bot. Gives trust recommendations when call is made Builds an internal representation of the trust
network from a collection of distributed sources. User can query from IRC channel, & the bot
returns the trust values Provides the weighted average, as well as
maximum and minimum path lengths, and maximum and minimum capacity paths
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Applications - TrustMail
Email client, developed on top of Mozilla Messenger
provides an inline trust rating for each email message
calls the web service, passing in the email address of the sender & mailbox address
If a user has a trust rating with respect to email, that value is used else general trust rating is used
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TrustMail Contd..Scenario
Two groups of people Each group has a Professor & set of students The two professors know each other & have their trust
ratings in trust Graph “My advisor has collaborated with you on this topic in the past
and she suggested I contact you.” Professor on receiving the email needs to verify either by
calling the other professor etc.. Using TrustMail reduces this by providing trust ratings for each
emails & may be with respect to the email subject topic
Their Claim TrustMail lowers the cost of sharing trust judgments across
widely dispersed and rarely interacting groups of people
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Related Works
Social Network & application of “small world” notion ”Small World” notion by Stanley Milgram, almost everybody
in the world are at most separated by “six degrees of separation”
Complex networks show this “small world” phenomenon Small average distance between two nodes, a high
connectance or clustering co-efficient “Smallworld” have been studied with respect to random
graphs. Studies have been undertaken with respect to spread of diseases between networks
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Related Works -Trust on the Semantic Web
Yolanda Gil and Varun Ratnakar Addressed trusting content and information sources Users included the credibility and reliability values while
annotating Their trust assessments were based on individual feedback
about the source of information Trust values are averaged and presented to the viewer. Uses TRELLIS system, users could view information,
annotations (averages of credibility, reliability etc) and then make analysis.
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Conclusions
Social Networks exists in the current web In current web, its hard to determine the topic
based on which the clustering (or social networks have been formed)
In Semantic Web everything is machine readable, & trust information can be annotated along with FOAF, so that trust can be associated with individuals in social networks
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Conclusions
Trust network is a directed graph with nodes forming the person and edges forming the trust weights
Trust value computed is based on the following Priority is given to direct link between two people Otherwise they try to find a weighted average of
the path between X & Y.
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Conclusion
Concept of trust and distrust is subjective, there can be several different metrics for inferring trust values between two people
Authors, do not concentrate of developing an optimal algorithm for computing trust
Authors focus on simple algorithm They try show some applications in which
trust ratings can be used- TrustMail
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Conclusion
Good thing about the paper is that they build trust networks on semantic web in a much more human sense.
They show that some of the applications like TrustMail can utilize the trust ratings.
Their claim is that Trust values can be inferred between two people even though there isn’t direct trust rating.
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References
Jennifer Golbeck link to Web of Trust, http://trust.mindswap.org/cgi-bin/trustBuilder.cgi
Trust Networks on the Semantic Web -Jennifer Golbeck, Bijan Parsia, James Hendler
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References
1. Adamic, L., "The Small World Web". Proceedings of ECDL, pages 443-- 452, 1999. 2. Adding SVG Paths to Co-Depiction RDF, http://Jibbering.com/svg/codepiction.html 3. The Advogato Website: http://www.advogato.org 4. Albert, R., Jeong, H. AND Barabasi, A.-L. "Diameter of the world-wide web." Nature 401, 130–131, 1999 5. Bharat, K and M.R. Henzinger. "Improved algorithms for topic distillation in a hyperlinked environment," Proc. ACM SIGIR, 1998. 6. Brin, S and L. Page, "The anatomy of a large-scale hypertextual Web search engine," Proc. 7th WWW Conf., 1998. 7. Broder, R Kumar, F. Maghoul, P. Raghavan, S. Rajagopalan, R. Stata, A. Tomkins, and J. Wiener. "Graph structure in the web. " Proc. 9th International World Wide Web Conference, 2000. 8. Carriere, J and R. Kazman, "WebQuery: Searching and visualizing the Web through connectivity," Proc. 6th WWW Conf., 1997. 9. Chakrabarti, S, B. Dom, D. Gibson, J. Kleinberg, P. Raghavan, and S. Rajagopalan, "Automatic resource compilation by analyzing hyperlink structure and associated text," Proc. 7th WWW Conf., 1998.
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References 10. Dumbill, Ed, “XML Watch: Finding friends with XML and RDF.” IBM Developer Works, http://www-106.ibm.com/developerworks/xml/library/xfoaf. html, June 2002. 11. FOAFNaut: http://foafnaut.org/ 12. Gil, Yolanda and Varun Ratnakar, "Trusting Information Sources One Citizen at a Time," Proceedings of the First International Semantic Web Conference (ISWC), Sardinia, Italy, June 2002. 13. Kleczkowski, A. and Grenfell, B. T. "Mean-fieldtype equations for spread of epidemics: The ‘small-world’ model." Physica A 274, 355–360, 1999. 14. Kleinberg, J, "Authoritative sources in a hyperlinked environment," Journal of the ACM, 1999. 15. Kumar, Ravi, Prabhakar Raghavan, Sridhar Rajagopalan, D. Sivakumar, Andrew Tomkins, and Eli Upfal. "The web as a graph". Proceedings of the Nineteenth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, May 15-17, 2000. 16. Labalme, Fen, Kevin Burton, "Enhancing the Internet with Reputations: An Openprivacy Whitepaper," http://www.openprivacy.org/papers/200103- white.html, March 2001. 17. Levien, Raph and Alexander Aiken. "Attack resistant trust metrics for public key certification." 7th USENIX Security Symposium, San Antonio, Texas, January 1998.
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References 18. Milgram, S. "The small world problem." Psychology Today 2, 60–67, 1967. 19. Moore, C. and Newman, M. E. J. "Epidemics and percolation in small-world 20. Newman, Mark, "The structure of scientific collaboration networks," Proc. Natl. Acad. Sci. USA 98, 404-409 (2001). 21. Newman, Mark, "Models of the small world", J. Stat. Phys. 101, 819-841 (2000). 22. Open Privacy Initiative: http://www.openprivacy.org/ 23. Mutton, Paul and Jennifer Golbeck, "Visualization of Semantic Metadata and Ontologies, " Proceedings of Information Visualization 2003, London, England, July 2003. 24. RDFWeb: FOAF: ‘the friend of a friend vocabulary’, http://rdfweb.org/foaf/ 25. RDFWeb: Co-depiction Photo Meta Data: http://rdfweb.org/2002/01/photo/ 26. Spertus, E, "ParaSite: Mining structural information on the Web," Proc. 6th WWW Conf., 1997. 27. Szalay, A. S. 2001, "Astronomical Data Analysis Software and Systems X," in ASP Conf. Ser., Vol. 238, eds. F. R. Harnden, Jr., F. A. Primini, & H. E. Payne (San Francisco: ASP), 3. 28. The Trust Ontology: http://www.mindswap.org/~golbeck/web/trust.daml 29. Watts, D. and S. H. Strogatz. "Collective Dynamics of Small-World' Networks", Nature 393:440-442 (1998)