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On Building and Updating Distributed LSI for P2P Systems

  • Sanfeng Zhang
  • Guoxin Wu
  • Gang Chen
  • Libo Xu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3759)

Abstract

Recently published studies have shown that Latent Semantic Indexing (LSI) plays an important role in content-based full text information retrieval of P2P system. However, it is a challenging problem to generate global consistent LSI structures in P2P systems because their nodes are self-organizing and their corpora are large, dynamic and distributed on different nodes. In this paper we propose a method for building LSI structures from distributed corpora. Our method is consisted with a network model for semantic information sampling and exchanging and a Reduced-Dimension-Representation (RDR)s merging algorithm. By the signal and noise subspace model, we also provide a theoretical justification that the RDR merging algorithm is sound. A simple numerical experiment shows that our RDR merging algorithm can keep query precision on an acceptable level.

Keywords

Singular Value Decomposition Vector Space Model Latent Semantic Indexing Document Vector Document Matrix 
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.

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References

  1. 1.
    Tang, C., Xu, Z., Dwarkadas, S.: Peer-to-Peer Information Retrieval Using Self-Organizing Semantic Overlay Networks. In: SIGCOMM 2003 (2003)Google Scholar
  2. 2.
    Tang, C., Xu, Z., Dwarkadas, S.: On Scaling Latent Semantic Indexing for Large Peer-to-Peer Systems. In: Proceedings of the 27th annual international conference on Research and development in information retrieval, pp. 112–121 (2004)Google Scholar
  3. 3.
    Shen, H.T., Shu, Y.F., Yu, B.: Efficient Semantic-Based Content Search in P2P Network. In: IEEE Transactions on Knowledge and Data Engineering archive, vol. 16, pp. 813–826 (2004)Google Scholar
  4. 4.
    Berry, M., Drmac, Z., Jessup, E.: Matrices, Vector Spaces, and Information Retrieval. SIAM Review 41(2), 335–362 (1999)zbMATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Berry, M.W., Dumais, S.T., O’Brien, G.W.: Using linear algebra for intelligent information retrieval. SIAM Review 37(4), 573–595 (1995)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Deerwester, S., Dumais, S.T.: Furnas. Indexing by latent semantic analysis. Journal of the American Society for Information Science 41(6), 391–407 (1990)CrossRefGoogle Scholar
  7. 7.
    Zha, H., Simon, H.: On Updating Problems in Latent Semantic Indexing. SIAM Journal of Scientific Computing 21, 782–791 (1999)zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Ratnasamy, S., Francis, P., Handley, M., Karp, R., Shenker, S.: A Scalable Content-Addressable Network. In: Proc. SIGCOMM (2001)Google Scholar
  9. 9.
    Stoica, I., Morris, R., Karger, D., Kaashoek, F., Balakrishnan, H.: Chord: A Scalable Peer-to-Peer Lookup Service for Internet Applications. In: Proc. SIGCOMM (2001)Google Scholar
  10. 10.
    Rowstron, A., Druschel, P.: Pastry: Scalable, distributed object location and routing for large-scale peer-to-peer systems. IFIP/ACM Middleware, Heidellberg, Germany, (November 2001)Google Scholar
  11. 11.
    Yang, B., Garcia-Molina, H.: Designing a super-peer network. In: ICDE (2003)Google Scholar
  12. 12.
    Cornell Smart System, ftp://ftp.cs.cornell.edu/pub/smart
  13. 13.
  14. 14.

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Sanfeng Zhang
    • 1
  • Guoxin Wu
    • 1
  • Gang Chen
    • 1
  • Libo Xu
    • 1
  1. 1.Department of Computer Science and EngineeringSoutheast UniversityNanjingChina

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