Latent Semantic Analysis – The Dynamics of Semantics Web Services Discovery

  • Chen Wu
  • Vidyasagar Potdar
  • Elizabeth Chang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4891)


Semantic Web Services (SWS) have currently drawn much momentum in both academia and industry. Most of the solutions and specifications for SWS rely on ontology building, a task needs much human (e.g. domain experts) involvement, and hence cannot scale very well in face of vast amount of web information and myriad of services providers. The recent proliferation of SOA applications exacerbates this issue by allowing loosely-coupled services to dynamically collaborate with each other, each of which might maintain a different set of ontology. This chapter presents the fundamental mechanism of Latent Semantic Analysis (LSA), an extended vector space model for Information Retrieval (IR), and its application in semantic web services discovery, selection, and aggregation for digital ecosystems. First, we explore the nature of current semantic web services within the principle of ubiquity and simplicity. This is followed by a succinct literature overview of current approaches for semantic services/software component (e.g. ontology-based OWL-s) discovery and the motivation for introducing LSA into the user-driven scenarios for service discovery and aggregation. We then direct the readers to the mathematical foundation of LSA - SVD of data matrices for calculating statistics distribution and thus capturing the ‘hidden’ semantics of web services concepts. Some existing applications of LSA in various research fields are briefly presented, which gives rise to the analysis of the uniqueness (i.e. strength, limitations, parameter settings) of LSA application in semantic web services. We provide a conceptual level solution with a proof-of-concept prototype to address such uniqueness. Finally we propose an LSA-enabled semantic web services architecture fostering service discovery, selection, and aggregation in a digital ecosystem.


Service Discovery Latent Semantic Analysis Vector Space Model Semantic Space Latent Semantic Indexing 
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.


  1. 1.
    Sajjanhar, A., Hou, J., Zhang, Y.: Algorithm for web services matching. In: Yu, J.X., Lin, X., Lu, H., Zhang, Y. (eds.) APWeb 2004. LNCS, vol. 3007, pp. 665–670. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  2. 2.
    Deerwester, S., Dumais, S., Furnas, G.W., Landauer, T.K., Harshamn, R.: Indexing by Latent Semantic Analysis. Journal of the American Society for Information Science 41, 391–407 (1990)CrossRefGoogle Scholar
  3. 3.
    Berry, M.W.: Large scale singular value computations. International Journal of Supercomputer Applications 6, 13–49 (1992)Google Scholar
  4. 4.
    Horst, P.: Factor Analysis of Data Matrices: Holt. Rinehart and Winston, Inc. (1965)Google Scholar
  5. 5.
    Eckart, C., Young, G.: The approximation of one matrix by another of lower rank. Psychometrika 1, 211–218 (1936)CrossRefzbMATHGoogle Scholar
  6. 6.
    Bartell, B.T., Cottrell, G.W., Belew, R.K.: Latent Semantic Indexing is an Optimal Special Case of Multidimensional Scaling. In: 15th Annual International SIGIR, Denmark (1992)Google Scholar
  7. 7.
    Caron, J.: Experiments with LSA Scoring: Optimal Rank and Basis, Computer Science Department, University of Colorado at Boulder (2000)Google Scholar
  8. 8.
    Landauer, T.K., Foltz, P.W., Laham, D.: Introduction to Latent Semantic Analysis. Discourse Processes 25, 259–284 (1998)CrossRefGoogle Scholar
  9. 9.
    Furnas, G.W., Deerwester, S., Dumais, S., Landauer, T.K., Harshamn, R.A., Streeter, L.A., Lochbaum, K.E.: Information Retrieval using a Singular Value Decomposition Model of Latent Semantic Structure (1988)Google Scholar
  10. 10.
    Skoyles, J.R.: Meaning and context: the implications of LSA (latent semantic analysis) for semantics (2000)Google Scholar
  11. 11.
    Yu, C., Cuadrado, J., Ceglowski, M., Payne, J.S.: Patterns in Unstructured Data Discovery, Aggregation, and Visualization (2005)Google Scholar
  12. 12.
    Lin, M.Y., Amor, R., Tempero, E.: A Java reuse repository for Eclipse using LSI. In: Australian Software Engineering Conference (2006)Google Scholar
  13. 13.
    Ye, Y.: Supporting component-based software development with active component retrieval systems. In: Computer Science, University of Colorado (2001)Google Scholar
  14. 14.
    Landauer, T., Laham, D., Rehder, R., Schreiner, M.E.: How well can passage meaning be derived without using word order? a comparison of Latent Semantic Analysis and humans. In: 19th Annual Conference of the Cognitive Science Society, Mahwah, NJ. USA (1997)Google Scholar
  15. 15.
    Kintsch, W.: Predication. Cognitive Science 25, 173–202 (2001)CrossRefGoogle Scholar
  16. 16.
    Wiemer-Hastings, P., Wiemer-Hastings, K., Graesser, A.: How latent is Latent Semantic Analysis? In: Sixteenth International Joint Congress on Artificial Intelligence, San Francisco. US (1999)Google Scholar
  17. 17.
    Dennis, S.: Introducing word order. In: McNamara, D., Landauer, T., Dennis, S., Kintsch, W. (eds.) LSA: A Road to Meaning. Erlbaum, Mahwah (2005)Google Scholar
  18. 18.
    Hu, X., Cai, Z., Franceschetti, D., Penumatsa, P., Graesser, A.C., Louwerse, M.M., McNamara, D.S.: LSA: The first dimension and dimensional weighting. In: 25th Annual Conference of the Cognitive Science Society (2003)Google Scholar
  19. 19.
    Denhière, G., Lemaire, B., Bellisens, C., Jhean, S.: A semantic space for modeling a child semantic memory. In: McNamara, D., Landauer, T., Dennis, S., Kintsch, W. (eds.) A Road to Meaning, Mahwah, NJ (2005)Google Scholar
  20. 20.
    Kittredge, R., Lehrberger, J.: Sublanguage: Studies of Language in Restricted Semantic Domains. de Gruyter (1982)Google Scholar
  21. 21.
    Duff, I., Grimes, R., Lewis, J.: Sparse Matrix Test Problems. ACM Transactions on Mathematical Software 15, 1–14 (1989)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Fan, J., Kambhampati, S.: A Snapshot of Public Web Services. ACM SIGMOD Record 34, 24–32 (2005)CrossRefGoogle Scholar
  23. 23.
    Berry, M.W., Drmac, Z., Jessup, E.R.: Matrices, Vector Spaces, and Information Retrieval. SIAM Review 41, 335–362 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison Wesley, Reading (1999)Google Scholar
  25. 25.
    Dumais, S.T.: Improving the retrieval of information from external sources. Behavior Research Methods, Instruments and Computers 23, 229–236 (1991)CrossRefGoogle Scholar
  26. 26.
    Berry, D.M., Do, T., O’Brien, G.W., Krishna, V., Varadhan, S.: SVDPACKC (Version 1.0) User’s Guide, Computer Science Department, Univeristy of Tennessee (1993)Google Scholar
  27. 27.
    Herrmann, M., Ahtisham Aslam, M., Dalferth, O.: Applying Semantics (WSDL, WSDL-S, OWL) in Service Oriented Architectures (SOA)Google Scholar
  28. 28.
    Cardoso, J., Sheth, A.P.: Semantic Web Services, Processes and Applications. Springer, Heidelberg (2006)CrossRefzbMATHGoogle Scholar
  29. 29.
    OWL-S semantic markup of web services – white paper (accessed on June 15, 2007),
  30. 30.
    Marinchev, I., Agre, G.: Semantically Annotating Web Services Using WSMO Technologies. Cybernetics and Information Technologies 5(2) (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Chen Wu
    • 1
  • Vidyasagar Potdar
    • 1
  • Elizabeth Chang
    • 1
  1. 1.Digital Ecosystems and Business Intelligence InstituteCurtin University of TechnologyPerthAustralia

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