Abstract
The database and information retrieval communities have long been recognized as being irreconcilable. Today, however, we witness a surprising convergence of the techniques used by both communities in decentralized, large-scale environments. The newly emerging field of reputation based trust management, borrowing techniques from both communities, best demonstrates this claim. We argue that incomplete knowledge and increasing autonomy of the participating entities are the driving forces behind this convergence, pushing the adoption of probabilistic techniques typically borrowed from an information retrieval context. We argue that using a common probabilistic framework would be an important step in furthering this convergence and enabling a common treatment and analysis of distributed complex systems. We will provide a first sketch of such a framework and illustrate it with examples from our previous work on information retrieval, structured search and trust assessment.
The work presented in this paper was supported (in part) by the National Competence Center in Research on Mobile Information and Communication Systems (NCCR-MICS) and by the Computational Reputation Mechanisms for Enabling Peer-to-Peer Commerce in Decentralized Networks Project, both supported by the Swiss National Science Foundation under grant number 5005-67322 and 20512-105287/1 respectively.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Keywords
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
Aberer, K., Cudré-Mauroux, P., Hauswirth, M.: Start making sense: The Chatty Web approach for global semantic agreements. Journal of Web Semantics 1(1) (2003)
Aberer, K., Cudré-Mauroux, P., Hauswirth, M.: The Chatty Web: Emergent Semantics Through Gossiping. In: International World Wide Web Conference (WWW) (2003)
Deshpande, A., Guestrin, C., Madden, S., Hellerstein, J.M., Hong, W.: Model-Driven Data Acquisition in Sensor Networks. In: Very Large DataBases (VLDB), pp. 588–599 (2004)
Despotovic, Z., Aberer, K.: A Probabilistic Approach to Predict Peers’ Performance in P2P Networks. In: Klusch, M., Ossowski, S., Kashyap, V., Unland, R. (eds.) CIA 2004. LNCS (LNAI), vol. 3191, pp. 62–76. Springer, Heidelberg (2004)
Fuhr, N.: Models in Information Retrieval. In: Agosti, M., Crestani, F., Pasi, G. (eds.) ESSIR 2000. LNCS, vol. 1980, p. 21. Springer, Heidelberg (2001)
Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank Citation Ranking: Bringing Order to the Web. Technical report, Stanford University, Stanford, CA (1998)
Pearl, J.: Causality: Models, Reasoning, and Inference. Cambridge University Press, Cambridge (2000)
Richardson, M., Agrawal, R., Domingos, P.: Trust management for the semantic web. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 351–368. Springer, Heidelberg (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Aberer, K., Cudré-Mauroux, P., Despotovic, Z. (2005). On the Convergence of Structured Search, Information Retrieval and Trust Management in Distributed Systems. In: Eymann, T., Klügl, F., Lamersdorf, W., Klusch, M., Huhns, M.N. (eds) Multiagent System Technologies. MATES 2005. Lecture Notes in Computer Science(), vol 3550. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550648_1
Download citation
DOI: https://doi.org/10.1007/11550648_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28740-7
Online ISBN: 978-3-540-28741-4
eBook Packages: Computer ScienceComputer Science (R0)