Advertisement

Trust Management for the Semantic Web

  • Matthew Richardson
  • Rakesh Agrawal
  • Pedro Domingos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2870)

Abstract

Though research on the Semantic Web has progressed at a steady pace, its promise has yet to be realized. One major difficulty is that, by its very nature, the Semantic Web is a large, uncensored system to which anyone may contribute. This raises the question of how much credence to give each source. We cannot expect each user to know the trustworthiness of each source, nor would we want to assign top-down or global credibility values due to the subjective nature of trust. We tackle this problem by employing a web of trust, in which each user maintains trusts in a small number of other users. We then compose these trusts into trust values for all other users. The result of our computation is not an agglomerate “trustworthiness" of each user. Instead, each user receives a personalized set of trusts, which may vary widely from person to person. We define properties for combination functions which merge such trusts, and define a class of functions for which merging may be done locally while maintaining these properties. We give examples of specific functions and apply them to data from Epinions and our BibServ bibliography server. Experiments confirm that the methods are robust to noise, and do not put unreasonable expectations on users. We hope that these methods will help move the Semantic Web closer to fulfilling its promise.

Keywords

Aggregation Function Trust Management Probabilistic Interpretation Path Algebra Combination Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Abdul-Rahman, A., Hailes, S.: A distributed trust model. In: Proceedings of New Security Paradigms Workshop, pp. 48–60 (1997)Google Scholar
  2. 2.
    Agrawal, R., Jagadish, H.V.: Multiprocessor transitive closure algorithms. In: Proceedings of the International Symposium on Databases in Parallel and Distributed Systems, Austin, TX, pp. 56–66 (1988)Google Scholar
  3. 3.
    Agrawal, R., Dar, S., Jagadish, H.V.: Direct transitive closure algorithms: Design and performance evaluation. ACM Transactions on Database Systems 15, 427–458 (1990)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Agresti, A.: Categorical data analysis. Wiley, New York (1990)zbMATHGoogle Scholar
  5. 5.
    Aho, A.V., Hopcroft, J.E., Ullman, J.D.: The design and analysis of computer algorithms. Addison-Wesley, Reading (1974)zbMATHGoogle Scholar
  6. 6.
    Ankolekar, A., Burstein, M.H., Hobbs, J.R., Lassila, O., Martin, D., McDermott, V., McIlraith, S.A., Narayanan, S., Paolucci, M., Payne, T.R., Sycara, K.P.: Daml-s: Web service description for the Semantic Web. In: Horrocks, I., Hendler, J. (eds.) ISWC 2002. LNCS, vol. 2342, pp. 348–363. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  7. 7.
    Bancilhon, F.: Naive evaluation of recursively defined relations. In: On Knowledge Base Management Systems (Islamorada), pp. 165–178 (1985)Google Scholar
  8. 8.
    Bellman, R., Giertz, M.: On the analytic formalism of the theory of fuzzy sets. Information Sciences 5, 149–156 (1973)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Berners-Lee, T., Hendler, J., Lassila, O.: The Semantic Web. Scientific American (May 2001)Google Scholar
  10. 10.
    Blaze, M., Feigenbaum, J., Lacy, J.: Decentralized trust management. In: Proceedings of the 1996 IEEE Symposium on Security and Privacy, Oakland, CA, pp. 164–173 (1996)Google Scholar
  11. 11.
    Carre, B.: Graphs and networks. Claredon Press, Oxford (1978)Google Scholar
  12. 12.
    Chakrabarti, S., Dom, B., Gibson, D., Kleinberg, J., Raghavan, P., Rajagopalan, S.: Automatic resource compilation by analyzing hyperlink structure and associated text. In: Proceedings of the Seventh International World Wide Web Conference, pp. 65–74. Elsevier, Brisbane (1998)Google Scholar
  13. 13.
    Chickering, D.M., Heckerman, D.: Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables. Machine Learning 29, 181–212 (1997)zbMATHCrossRefGoogle Scholar
  14. 14.
    Doan, A., Madhavan, J., Domingos, P., Halevy, A.Y.: Learning to Map between Ontologies on the Semantic Web. In: Proceedings of the Eleventh International World Wide Web Conference, pp. 662–673 (2002)Google Scholar
  15. 15.
    Doan, A., Domingos, P., Halevy, A.: Reconciling schemas of disparate data sources: A machine-learning approach. In: Proceedings of the 2001 ACM SIGMOD International Conference on Management of Data, pp. 509–520. ACM Press, Santa Barbara (2001)CrossRefGoogle Scholar
  16. 16.
    Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 57–66. ACM Press, San Francisco (2001)CrossRefGoogle Scholar
  17. 17.
    French, S.: Group consensus probability distributions: A critical survey. In: Bernardo, J.M., DeGroot, M.H., Lindley, D.V., Smith, A.F.M. (eds.) Bayesian statistics 2, pp. 183–202. Elsevier, Amsterdam (1985)Google Scholar
  18. 18.
    Genest, C., Zidek, J.V.: Combining probability distributions: A critique and an annotated bibliography. Statistical Science 1, 114–148 (1986)CrossRefMathSciNetGoogle Scholar
  19. 19.
    Gil, Y., Ratnakar, V.: Trusting information sources one citizen at a time. In: Horrocks, I., Hendler, J. (eds.) ISWC 2002. LNCS, vol. 2342, pp. 162–176. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  20. 20.
    Joachims, T.: A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization. In: Proceedings of the Fourteenth International Conference on Machine Learning (ICML 1997), pp. 143–151. Morgan Kaufmann, San Francisco (1997)Google Scholar
  21. 21.
    Kamvar, S., Schlosser, M., Garcia-Molina, H.: The EigenTrust algorithm for reputation management in P2P networks. In: Proceedings of the Twelfth International World Wide Web Conference (2003)Google Scholar
  22. 22.
    Kautz, H., Selman, B., Shah, M.: ReferralWeb: Combining social networks and collaborative filtering. Communications of the ACM 40, 63–66 (1997)CrossRefGoogle Scholar
  23. 23.
    Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. In: Proceedings of the Ninth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 668–677. ACM Press, Baltimore (1998)Google Scholar
  24. 24.
    Motwani, R., Raghavan, P.: Randomized algorithms. Cambridge University Press, Cambridge (1995)zbMATHGoogle Scholar
  25. 25.
    Ngo, L., Haddawy, P.: Answering queries from context-sensitive probabilistic knowledge bases. Theoretical Computer Science 171, 147–177 (1997)zbMATHCrossRefMathSciNetGoogle Scholar
  26. 26.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: Bringing order to the web (Technical Report). Stanford University, Stanford, CA (1998)Google Scholar
  27. 27.
    Patel-Schneider, P., Simeon, J.: Building the Semantic Web on XML. In: Horrocks, I., Hendler, J. (eds.) ISWC 2002. LNCS, vol. 2342, pp. 147–161. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  28. 28.
    Pearl, J.: Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann, San Francisco (1988)Google Scholar
  29. 29.
    Pennock, D.M., Nielsen, F.A., Giles, C.L.: Extracting collective probabilistic forecasts from Web games. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 174–183. ACM Press, San Francisco (2001)CrossRefGoogle Scholar
  30. 30.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: An open architecture for collaborative filtering of netnews. In: Proceedings of the ACM 1994 Conference on Computer Supported Cooperative Work, pp. 175–186. ACM Press, New York (1994)CrossRefGoogle Scholar
  31. 31.
    Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 61–70. ACM Press, Edmonton (2002)CrossRefGoogle Scholar
  32. 32.
    Richardson, M., Domingos, P.: The intelligent surfer: Probabilistic combination of link and content information in PageRank. In: Dietterich, T.G., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems 14, pp. 1441–1448. MIT Press, Cambridge (2002)Google Scholar
  33. 33.
    Warshall, S.: A theorem on boolean matrices. Journal of the ACM 9, 11–12 (1962)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Matthew Richardson
    • 1
  • Rakesh Agrawal
    • 2
  • Pedro Domingos
    • 1
  1. 1.University of WashingtonSeattleUSA
  2. 2.IBM Almaden Research CenterSan JoseUSA

Personalised recommendations