Automatic Service Categorisation through Machine Learning in Emergent Middleware

  • Amel Bennaceur
  • Valérie Issarny
  • Richard Johansson
  • Alessandro Moschitti
  • Romina Spalazzese
  • Daniel Sykes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7542)


The modern environment of mobile, pervasive, evolving services presents a great challenge to traditional solutions for enabling interoperability. Automated solutions appear to be the only way to achieve interoperability with the needed level of flexibility and scalability. While necessary, the techniques used to determine compatibility, as a precursor to interaction, come at a substantial computational cost, especially when checks are performed between systems in unrelated domains. To overcome this, we apply machine learning to extract high-level functionality information through text categorisation of a system’s interface description. This categorisation allows us to restrict the scope of compatibility checks, giving an overall performance gain when conducting matchmaking between systems. We have evaluated our approach on a corpus of web service descriptions, where even with moderate categorisation accuracy, a substantial performance benefit can be found. This in turn improves the applicability of our overall approach for achieving interoperability in the Connect project.


Network System Text Categorisation Interface Description Functional Semantic Compatibility Check 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Blair, G.S., Bennaceur, A., Georgantas, N., Grace, P., Issarny, V., Nundloll, V., Paolucci, M.: The Role of Ontologies in Emergent Middleware: Supporting Interoperability in Complex Distributed Systems. In: Kon, F., Kermarrec, A.-M. (eds.) Middleware 2011. LNCS, vol. 7049, pp. 410–430. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F.: The Description Logic Handbook. Cambridge University Press (2003)Google Scholar
  3. 3.
    Keller, R.M.: Formal verification of parallel programs. Commun. ACM (1976)Google Scholar
  4. 4.
    Liskov, B.: Keynote address - data abstraction and hierarchy. In: Addendum to the Proceedings on Object-Oriented Programming Systems, Languages and Applications (Addendum), OOPSLA 1987, pp. 17–34. ACM, New York (1987)Google Scholar
  5. 5.
    Calinescu, R., Kikuchi, S.: Formal Methods @ Runtime. In: Calinescu, R., Jackson, E. (eds.) Monterey Workshop 2010. LNCS, vol. 6662, pp. 122–135. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  6. 6.
    Baresi, L., Di Nitto, E., Ghezzi, C.: Toward open-world software: Issue and challenges. Computer (2006)Google Scholar
  7. 7.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing, University of Pennsylvania, United States, pp. 79–86 (2002)Google Scholar
  8. 8.
    Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Technical Report TR74-218, Department of Computer Science, Cornell University, Ithaca, New York (1974)Google Scholar
  9. 9.
    Moschitti, A., Basili, R.: Complex Linguistic Features for Text Classification: A Comprehensive Study. In: McDonald, S., Tait, J.I. (eds.) ECIR 2004. LNCS, vol. 2997, pp. 181–196. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    Moschitti, A.: Kernel methods, syntax and semantics for relational text categorization. In: Proceedings of ACM 17th Conference on Information and Knowledge Management, CIKM, Napa Valley, United States (2008)Google Scholar
  11. 11.
    Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: Liblinear: A library for large linear classification. Journal of Machine Learning Research 9, 1871–1874 (2008)zbMATHGoogle Scholar
  12. 12.
    Li, H., Du, X., Tian, X.: A WSMO-Based Semantic Web Services Discovery Framework in Heterogeneous Ontologies Environment. In: Zhang, Z., Siekmann, J.H. (eds.) KSEM 2007. LNCS (LNAI), vol. 4798, pp. 617–622. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Pirrò, G., Trunfio, P., Talia, D., Missier, P., Goble, C.A.: Ergot: A semantic-based system for service discovery in distributed infrastructures. In: CCGRID, pp. 263–272 (2010)Google Scholar
  14. 14.
    Oldham, N., Thomas, C., Sheth, A.P., Verma, K.: METEOR-S Web Service Annotation Framework with Machine Learning Classification. In: Cardoso, J., Sheth, A.P. (eds.) SWSWPC 2004. LNCS, vol. 3387, pp. 137–146. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Klusch, M., Kapahnke, P., Zinnikus, I.: Sawsdl-mx2: A machine-learning approach for integrating semantic web service matchmaking variants. In: ICWS, pp. 335–342 (2009)Google Scholar
  16. 16.
    Brandin, B., Wonham, W.: Supervisory control of timed discrete-event systems. IEEE Transactions on Automatic Control 39(2) (1994)Google Scholar
  17. 17.
    Ramadge, P., Wonham, W.: Supervisory control of a class of discrete event processes. SIAM J. Control and Optimization 25(1) (1987)Google Scholar
  18. 18.
    Bracciali, A., Brogi, A., Canal, C.: A formal approach to component adaptation. J. Syst. Softw. 74 (2005)Google Scholar
  19. 19.
    Calvert, K.L., Lam, S.S.: Formal methods for protocol conversion. IEEE Journal on Selected Areas in Communications 8(1), 127–142 (1990)CrossRefGoogle Scholar
  20. 20.
    Lam, S.S.: Correction to ”protocol conversion”. IEEE Trans. Software Eng. 14(9), 1376 (1988)CrossRefGoogle Scholar
  21. 21.
    Okumura, K.: A formal protocol conversion method. In: SIGCOMM, pp. 30–37 (1986)Google Scholar
  22. 22.
    Passerone, R., de Alfaro, L., Henzinger, T.A., Sangiovanni-Vincentelli, A.L.: Convertibility verification and converter synthesis: two faces of the same coin. In: Proceedings of the 2002 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2002, pp. 132–139 (2002)Google Scholar
  23. 23.
    Yellin, D.M., Strom, R.E.: Protocol specifications and component adaptors. ACM Trans. Program. Lang. Syst. 19 (1997)Google Scholar
  24. 24.
    Cimpian, E., Mocan, A.: WSMX Process Mediation Based on Choreographies. In: Bussler, C., Haller, A. (eds.) BPM 2005. LNCS, vol. 3812, pp. 130–143. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  25. 25.
    Vaculín, R., Neruda, R., Sycara, K.: An Agent for Asymmetric Process Mediation in Open Environments. In: Kowalczyk, R., Huhns, M.N., Klusch, M., Maamar, Z., Vo, Q.B. (eds.) SOCASE 2008. LNCS, vol. 5006, pp. 104–117. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  26. 26.
    Motahari Nezhad, H.R., Benatallah, B., Martens, A., Curbera, F., Casati, F.: Semi-automated adaptation of service interactions. In: WWW 2007: Proceedings of the 16th International Conference on World Wide Web, pp. 993–1002. ACM, New York (2007)Google Scholar
  27. 27.
    Williams, S.K., Battle, S.A., Cuadrado, J.E.: Protocol Mediation for Adaptation in Semantic Web Services. In: Sure, Y., Domingue, J. (eds.) ESWC 2006. LNCS, vol. 4011, pp. 635–649. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  28. 28.
    Motahari Nezhad, H.R., Xu, G.Y., Benatallah, B.: Protocol-aware matching of web service interfaces for adapter development. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, pp. 731–740. ACM, New York (2010)Google Scholar
  29. 29.
    Ponnekanti, S.R., Fox, A.: Interoperability Among Independently Evolving Web Services. In: Jacobsen, H.-A. (ed.) Middleware 2004. LNCS, vol. 3231, pp. 331–351. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  30. 30.
    Denaro, G., Pezzé, M., Tosi, D.: Ensuring interoperable service-oriented systems through engineered self-healing. In: Proceedings of ESEC/FSE 2009. ACM Press (2009)Google Scholar
  31. 31.
    Cavallaro, L., Di Nitto, E., Pradella, M.: An Automatic Approach to Enable Replacement of Conversational Services. In: Baresi, L., Chi, C.-H., Suzuki, J. (eds.) ICSOC-ServiceWave 2009. LNCS, vol. 5900, pp. 159–174. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  32. 32.
    Heß, A., Kushmerick, N.: Learning to Attach Semantic Metadata to Web Services. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 258–273. Springer, Heidelberg (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Amel Bennaceur
    • 1
  • Valérie Issarny
    • 1
  • Richard Johansson
    • 4
  • Alessandro Moschitti
    • 3
  • Romina Spalazzese
    • 2
  • Daniel Sykes
    • 1
  1. 1.INRIA, Paris-RocquencourtFrance
  2. 2.University of L’AquilaItaly
  3. 3.University of TrentoItaly
  4. 4.University of GothenburgSweden

Personalised recommendations