Information Systems Frontiers

, Volume 13, Issue 3, pp 407–428 | Cite as

Combining query-by-example and query expansion for simplifying web service discovery

  • Marco CrassoEmail author
  • Alejandro Zunino
  • Marcelo Campo


The vision of a worldwide computing network of services that Service Oriented Computing paradigm and its most popular materialization, namely Web Service technologies, promote is a victim of its own success. As the number of publicly available services grows, discovering proper services is similar to finding a needle in a haystack. Different approaches aim at making discovery more accurate and even automatic. However they impose heavy modifications over current Web Service infrastructures and require developers to invest much effort into publishing and describing their services and needs. So far, the acceptance of this paradigm is mainly limited by the high costs associated with connecting service providers and consumers. This paper presents WSQBE+, an approach to make Web Service publication and discovery easier. WSQBE+ combines open standards and popular best practices for using external Web services with text-mining and machine learning techniques. We describe our approach and empirically evaluate it in terms of retrieval effectiveness and processing time, by using a data-set of 391 public services.


Service oriented computing Web Service discovery Query expansion 



We thank Mariano Fischer and Matías Martinez for helping us implementing the query expansion techniques, the plug-in for Eclipse and structural matching techniques. We also thank the anonymous reviewers for their helpful comments and suggestions to improve the quality of the paper.


  1. Agichtein, E., Brill, E., Dumais, S., & Ragno, R. (2006). Learning user interaction models for predicting web search result preferences. In 29th annual international ACM SIGIR conference on research and development in information retrieval (pp. 3–10).Google Scholar
  2. Al-Masri, E., & Mahmoud, Q. H. (2007). Qos-based discovery and ranking of web services. In International conference on computer communications and networks (pp. 529–534).Google Scholar
  3. Baeza-Yates, R., & Ribeiro-Neto, B. (1999). Modern information retrieval. Reading: Addition Wesley.Google Scholar
  4. Bai, J., Nie, J.-Y., Cao, G., & Bouchard, H. (2007). Using query contexts in information retrieval. In SIGIR ’07: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval (pp. 15–22).Google Scholar
  5. Birukou, A., Blanzieri, E., D’Andrea, V., Giorgini, P., & Kokash, N. (2007). Improving web service discovery with usage data. IEEE Software, 24(6), 47–54.CrossRefGoogle Scholar
  6. Blake, B., Kahan, D., & Nowlan, M. (2007). Context-aware agents for user-oriented web services discovery and execution. Distributed and Parallel Databases, 21(1), 39–58.CrossRefGoogle Scholar
  7. Blake, B., Nowlan, M., & Kahan, D. (2008). Taming web services from the wild. IEEE Internet Computing, 12(5), 62–69.CrossRefGoogle Scholar
  8. Bollmann, P. (1983). The normalized recall and related measures. In Proceedings of the 6th annual international ACM SIGIR conference on research and development in information retrieval (pp. 122–128).Google Scholar
  9. Buckley, C., Salton, G., & Allan, J. (1994). The effect of adding relevance information in a relevance feedback environment. In SIGIR ’94: Proceedings of the 17th annual international ACM SIGIR conference on research and development in information retrieval.Google Scholar
  10. Burstein, M., Bussler, C., Zaremba, M., Finin, T., Huhns, M. N., Paolucci, M., et al. (2005). A semantic web services architecture. IEEE Internet Computing, 9(5), 72–81.CrossRefGoogle Scholar
  11. Cibrán, M. A., Verheecke, B., Vanderperren, W., Suvée, D., & Jonckers, V. (2007). Aspect-oriented programming for dynamic web service selection, integration and management. World Wide Web, 10(3), 211–242.CrossRefGoogle Scholar
  12. Crasso, M., Zunino, A., & Campo, M. (2008a). AWSC: An approach to web service classification based on machine learning techniques. Inteligencia Artificial, Revista Iberoamericana de IA, 37(12), 25–36.Google Scholar
  13. Crasso, M., Zunino, A., & Campo, M. (2008b). Easy Web Service discovery: A query-by-example approach. Science of Computer Programming, 71(2), 144–164.CrossRefGoogle Scholar
  14. Dong, X., Halevy, A. Y., Madhavan, J., Nemes, E., & Zhang, J. (2004). Similarity search for web services. In (e)Proceedings of the thirtieth international conference on very large data bases (pp. 372–383).Google Scholar
  15. Duftler, M., Mukhi, N., Slominski, A., & Weerawarana, S. (2001). Web services invocation framework (WSIF). In Workshop on object-oriented web services (OOWS ’01), ACM conference on object-oriented programming, systems, languages and applications (OOPSLA ’01). Tampa, Florida.Google Scholar
  16. Erl, T. (2005). Service-oriented architecture (SOA): Concepts, technology, and design. Englewood Cliffs: Prentice Hall.Google Scholar
  17. Fellbaum, C. (1989). WordNet: An electronic lexical database. Scituate: Bradford Books.Google Scholar
  18. Fensel, D., Lausen, H., de Bruijn, J., Stollberg, M., Roman, D., & Polleres, A. (2006). Enabling semantic web services: The web service modelling ontology. New York: Springer.Google Scholar
  19. Garofalakis, J., Panagis, Y., Sakkopoulos, E., & Tsakalidis, A. (2006). Contemporary web service discovery mechanisms. Journal of Web Engineering, 5(3), 265–290.Google Scholar
  20. Gomez-Pérez, A., Corcho-García, O., & Fernández-López, M. (2003). Ontological engineering. New York: Springer.Google Scholar
  21. Gotthelf, P., Zunino, A., Mateos, C., & Campo, M. (2008). GMAC: An overlay multicast network for mobile agent platforms. Journal of Parallel Distributed Computing, 68(8), 1081–1096.CrossRefGoogle Scholar
  22. Hatcher, E., & Gospodnetic, O. (2004). Lucene in action (in action series). Bellows Falls: Manning.Google Scholar
  23. Heß, A., Johnston, E., & Kushmerick, N. (2004). ASSAM: A tool for semi-automatically annotating semantic web services. In International semantic web conference. Lecture notes in computer science (LNCS) (Vol. 3298, pp. 320–334).Google Scholar
  24. Huhns, M., & Singh, M. (2005). Service-oriented computing: Key concepts and principles. IEEE Internet Computing, 9(1), 75–81.CrossRefGoogle Scholar
  25. Jennings, N., & Wooldridge, M. (1996). Software agents. IEE Review, 42(1), 17–20.CrossRefGoogle Scholar
  26. Joachims, T. (1997). A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization. In International conference on machine learning (pp. 143–151).Google Scholar
  27. Johnson, R. (2005). J2EE development frameworks. Computer, 38(1), 107–110.CrossRefGoogle Scholar
  28. Kim, M.-C., & Choi, K.-S. (1999). A comparison of collocation-based similarity measures in query expansion. Information Processing & Management, 35(1), 19–30.CrossRefGoogle Scholar
  29. Kittredge, R. I. (1982). Sublanguages. American Journal of Computational Linguistics, 8(2), 79–84.Google Scholar
  30. Korfhage, R. (1997). Information storage and retrieval. New York: Wiley.Google Scholar
  31. Kozlenkov, A., Spanoudakis, G., Zisman, A., Fasoulas, V., & Cid, F. S. (2007). Architecture-driven service discovery for service centric systems. International Journal of Web Services research, 4(2), 82–113.CrossRefGoogle Scholar
  32. Losee, R. (1995). Sublanguage terms: Dictionaries, usage, and automatic classification. Journal of the American Society for Information Science, 46(7), 519–529.CrossRefGoogle Scholar
  33. Mateos, C., Crasso, M., Zunino, A., & Campo, M. (2006). Supporting ontology-based semantic matching of web services in MoviLog. In Advances in artificial intelligence, 2nd international joint conference: 10th Ibero-American conference on AI, 18th Brazilian AI symposium (IBERAMIA-SBIA 2006). Lecture notes in computer science (LNCS) (Vol. 4140).Google Scholar
  34. McConnell, S. (2006). Software estimation: Demystifying the black art. Redmond: Microsoft.Google Scholar
  35. McCool, R. (2005). Rethinking the semantic web. Part I. IEEE Internet Computing, 9(6), 88, 86–87.CrossRefGoogle Scholar
  36. McIlraith, S., & Martin, D. (2003) Bringing semantics to web services. IEEE Intelligent Systems, 18(1), 90–93.CrossRefGoogle Scholar
  37. Paolucci, M., & Sycara, K. (2003) Autonomous semantic web services. IEEE Internet Computing, 7(5), 34–41.CrossRefGoogle Scholar
  38. Papazoglou, M., Traverso, P., Dustdar, S., & Leymann, F. (2007). Service-oriented computing: State of the art and research challenges. Computer, 40(11), 38–45.CrossRefGoogle Scholar
  39. Papazoglou, M., & Heuvel, W.-J. (2007). Service oriented architectures: Approaches, technologies and research issues. The VLDB Journal, 16(3), 389–415.CrossRefGoogle Scholar
  40. Porter, M. (1997). An algorithm for suffix stripping. Readings in information retrieval (pp. 313–316).Google Scholar
  41. Pu, K., Hristidis, V., & Koudas, N. (2006) Syntactic rule based approach toweb service composition. In ICDE ’06: Proceedings of the 22nd international conference on data engineering (p. 31).Google Scholar
  42. Rocchio, J. (1971). Relevance feedback in information retrieval. In The smart retrieval system—experiments in automatic document processing (pp. 313–323).Google Scholar
  43. Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513–523.CrossRefGoogle Scholar
  44. Schmidt, C., & Parashar, M. (2004) A peer-to-peer approach to web service discovery. World Wide Web, 7(2), 211–229.CrossRefGoogle Scholar
  45. Shadbolt, N., Berners-Lee, T., & Hall, W. (2006). The semantic web revisited. IEEE Intelligent Systems, 21(3), 96–101.CrossRefGoogle Scholar
  46. Shamsfard, M., & Barforoush, A. A. (2004). Learning ontologies from natural language texts. International Journal of Human-Computer Studies, 60, 17–63.CrossRefGoogle Scholar
  47. Sivashanmugam, K., Verma, K., Sheth, A., & Miller, J. A. (2003). Adding semantics to web services standards. In The 2003 international conference on web services (pp. 395–401).Google Scholar
  48. Spinellis, D. (2008). The way we program. IEEE Software, 25(4), 89–91.CrossRefGoogle Scholar
  49. Stairmand, M. (1997). Textual context analysis for information retrieval. SIGIR Forum, 31(SI), 140–147.CrossRefGoogle Scholar
  50. Stroulia, E., & Wang, Y. (2005). Structural and semantic matching for assessing web service similarity. International Journal of Cooperative Information Systems, 14(4), 407–438.CrossRefGoogle Scholar
  51. Vinoski, S. (2005). A time for reflection [software reflection]. IEEE Internet Computing, 9(1), 86–89.CrossRefGoogle Scholar
  52. Voorhees, E. (1993). Using WordNet to disambiguate word senses for text retrieval. In Proceedings of the 16th annual international ACM SIGIR conference on research and development in information retrieval (pp. 171–180).Google Scholar
  53. Wang, H., Huang, J. Z., Qu, Y., & Xie, J. (2004). Web services: Problems and future directions. Journal of Web Semantics, 1(3), 309–320.CrossRefGoogle Scholar
  54. Weerawarana, S., Curbera, F., Leymann, F., Storey, T., & Ferguson, D. F. (2005). Web services platform architecture: SOAP, WSDL, WS-policy, WS-addressing, WS-BPEL, WS-reliable messaging, and more. Englewood Cliffs: Prentice Hall.Google Scholar
  55. Whang, K.-Y., Ammann, A., Bolmarcich, A., Hanrahan, M., Hochgesang, G., Huang, K.-T., et al. (1987). Office-by-example: An integrated office system and database manager. ACM Transactions on Information Systems, 5(4), 393–427.CrossRefGoogle Scholar
  56. Witte, R., Li, Q., Zhang, Y., & Rilling, J. (2008). Text mining and software engineering: An integrated source code and document analysis approach. IET Software Journal, 2, 3–16.CrossRefGoogle Scholar
  57. Zhuge, H., & Liu, J. (2004). Flexible retrieval of web services. Journal of Systems and Software, 70(1–2), 107–116.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Marco Crasso
    • 1
    • 2
    Email author
  • Alejandro Zunino
    • 1
    • 2
  • Marcelo Campo
    • 1
    • 2
  1. 1.ISISTAN Research InstituteUniversidad Nacional del Centro de la provincia de Buenos Aires (UNCPBA)Buenos AiresArgentina
  2. 2.Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)Buenos AiresArgentina

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