Trust on the Semantic Web Seyyed asgary ghasempouri Sharif University of Technology Computer...

Trust on the Semantic Web

Seyyed asgary ghasempouri

Sharif University of TechnologyComputer Department

Sharif University of Technology Computer 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


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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)


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