Service Oriented Computing and Applications

, Volume 11, Issue 3, pp 285–299 | Cite as

Creating and utilizing section-level Web service tags in service replaceability

Original Research Paper


Web Services are used as reusable components in different types of applications. The correct discovery, combination and sequential use are main problems in Web service-based applications. In order to solve these issues, service matchmaking, classification and clustering techniques are usually proposed including Web service tagging. However, more specific problems of service matchmaking, such as service replaceability, require annotation techniques at finer levels of granularity in order to draw more detailed conclusions on the service use. In this work, a technique for service annotation on Web service description sections is proposed that focuses on port type, operation and message sections. Different classification algorithms are employed in section-level tagging. The use of this technique in the service matchmaking problem of service replaceability is demonstrated. Both techniques have been evaluated on datasets of real Web Services demonstrating the usefulness of the automated process for section-level service annotation and of the suggested matches for service replaceability.


Web service Web service annotations Web Services Description Language Service replaceability 


  1. 1.
    AbuJarour M, Naumann F, Craculeac M (2010) Collecting, annotating, and classifying public web services. In: 2010 international conference on the move to meaningful internet systems, pp 256–272Google Scholar
  2. 2.
    Aha DW, Kibler D (1991) Instance-based learning algorithms. Mach Learn 6:37–66MATHGoogle Scholar
  3. 3.
    Andrikopoulos V, Pierluigi P (2011) Retrieving compatible web services. In: 2011 IEEE international conference on web services (ICWS 11), pp 179–186Google Scholar
  4. 4.
    Andrikopoulos V, Benbernou S, Papazoglou MP (2012) On the evolution of services. IEEE Trans Softw Eng 38(3):609–628CrossRefGoogle Scholar
  5. 5.
    Azmeh Z, Falleri JR, Huchard M, Tibermacine C (2011) Automatic web service tagging using machine learning and wordnet synsets. Web information systems and technologies. Springer, BerlinGoogle Scholar
  6. 6.
    Aznag M, Quafafou M, Rochd EM, Jarir Z (2013) Probabilistic topic models for web services clustering and discovery. European conference on service-oriented and cloud computing. Springer, Berlin, pp 19–33CrossRefGoogle Scholar
  7. 7.
    Belhajjame K, Embury SM, Paton NW (2014) Verification of semantic web service annotations using ontology-based partitioning. IEEE Trans Serv Comput 7(3):515–528CrossRefGoogle Scholar
  8. 8.
    Bertolino A, Blake MB, Mehra P, Mei H, Xie T (2015) Software engineering for internet computing: internetware and beyond [guest editors’ introduction]. IEEE Softw 32(1):35–37CrossRefGoogle Scholar
  9. 9.
    Chen L, Wu J, Zheng Z, Lyu MR, Wu Z (2014) Modeling and exploiting tag relevance for web service mining. Knowl Inf Syst 39(1):153–173CrossRefGoogle Scholar
  10. 10.
    Chen N, Hoi SCH, Li S, Xiao, X (2016) Mobile app tagging. In: 9th ACM international conference on web search and data mining (WSDM ’16), ACM, pp 63–72Google Scholar
  11. 11.
    Elgazzar, K, Hassan AE, Martin P (2010) Clustering WSDL documents to bootstrap the discovery of web services. In: IEEE international conference on web services (ICWS 10), pp 147–154Google Scholar
  12. 12.
    Fang L, Wang L, Li, M, Zhao J, Zou Y, Shao L (2012) Towards automatic tagging for web services. In: IEEE 19th international conference on web services (ICWS 12), pp 528–535Google Scholar
  13. 13.
    Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten JH (2009) The WEKA data mining software: an update. ACM SIGKDD Explor 11:1CrossRefGoogle Scholar
  14. 14.
    John GH, Langle P (1995) Estimating continuous distributions in Bayesian classifiers. In: 11th conference on uncertainty in artificial intelligence, pp 338–345Google Scholar
  15. 15.
    Kapitsaki GM (2012) Web service matchmaking for the development of context-aware applications. IET Softw 6:536–548CrossRefGoogle Scholar
  16. 16.
    Kapitsaki GM, Achilleos A (2012) Model matching for Web Services on context dependencies. In: 14th international conference on information integration and web-based applications and services (IIWAS 12), ACM, pp 45–53Google Scholar
  17. 17.
    Kapitsaki GM (2014) Annotating web service sections with combined classification. In: IEEE 21st international conference on web services (ICWS 14), pp 622–629Google Scholar
  18. 18.
    Kopecky J, Vitvar T, Bournez C, Farrell J (2007) SAWSDL: semantic annotations for WSDL and XML schema. IEEE Internet Comput 11(6):60–67CrossRefGoogle Scholar
  19. 19.
    Kumara BT, Yaguchi Y, Paik I, Chen W (2013) Clustering and spherical visualization of web services. In: IEEE international conference on services computing (SCC 13), pp 89–96Google Scholar
  20. 20.
    Lemos AL, Danie F, Benatallah B (2016) Web service composition: a survey of techniques and tools. ACM Comput Surv (CSUR) 48(3):33Google Scholar
  21. 21.
    Li L, Zhang C (2014) Quality evaluation of social tags according to web resource types. In: WWW ’14 companion: proceedings of the 23rd international conference on world wide web, ACM, pp 1123–1128Google Scholar
  22. 22.
    Liang T, Chen L, Ying H, Wu J (2014) Co-clustering WSDL documents to bootstrap service discovery. In: IEEE 7th international conference on service-oriented computing and applications (SOCA 14), pp 215–222Google Scholar
  23. 23.
    Lin, M, Cheung DW (2014) Automatic tagging web services using machine learning techniques. In: 2014 IEEE/WIC/ACM international joint conferences on web intelligence (WI) and intelligent agent technologies (IAT), 02, pp 258–265Google Scholar
  24. 24.
    Lu J, Rosenblum DS, Bultan T, Issarny V, Dustdar S, Storey MA, Zhang D (2015) Roundtable: the future of software engineering for internet computing. IEEE Softw 32(1):91–97CrossRefGoogle Scholar
  25. 25.
    Luo C, Zheng Z, Wu X, Yang F, Zhao Y (2016) Automated structural semantic annotation for RESTful services. Int J Web Grid Serv 12(1):26–31CrossRefGoogle Scholar
  26. 26.
    Ma J, Zhang Y, He J (2008) Efficiently finding web services using a clustering semantic approach. In: 2008 international workshop on Context enabled source and service selection, integration and adaptation: organized with the 17th International world wide web Conference, ACM, 5Google Scholar
  27. 27.
    Park J-H (2014) Spatial semantic search in location-based web services. In: 23rd international conference on world wide web, ACM, pp 9–14Google Scholar
  28. 28.
    Pleban P, Pernici B (2009) URBE: web service retrieval based on similarity evaluation. IEEE Trans Knowl Data Eng 21(11):1629–1642CrossRefGoogle Scholar
  29. 29.
    Porter MF (1980) An algorithm for suffix stripping. Program Electron Libr Inf Syst 14(3):130–137CrossRefGoogle Scholar
  30. 30.
    Quinlan R (1993) C4.5: programs for machine learning. Morgan Kaufmann Publishers, San MateoGoogle Scholar
  31. 31.
    Robie J, Cavicchio R, Sinnema R, Wilde E (2013) RESTful service description language (RSDL): describing RESTful services without tight coupling. In: the Balisage: the markup conference, 10Google Scholar
  32. 32.
    Salton G, McGill MJ (1983) An introduction to modern information retrieval. McGraw-Hill Inc, New YorkMATHGoogle Scholar
  33. 33.
    Stark MM, Riesenfeld RF (1998) Wordnet: an electronic lexical database. In: 11th eurographics workshop on rendering. MIT PressGoogle Scholar
  34. 34.
    Stroulia E, Wang Y (2005) Structural and semantic matching for assessing web-service similarity. Int J Cooper Inf Syst 14(04):407–437CrossRefGoogle Scholar
  35. 35.
    Suen CY (1979) N-gram statistics for natural language under-standing and text processing. IEEE Trans Pattern Anal Mach Intell 1(2):164–172CrossRefGoogle Scholar
  36. 36.
    Tinsley HE, Weiss DJ (2000) Interrater reliability and agreement. In: Tinsley HE, Brown SD (eds) Handbook of applied multivariate statistics and mathematical modeling. Academic Press, San Diego, pp 95–124Google Scholar
  37. 37.
    Winkler WE (1990) String comparator metrics and enhanced decision rules in the Fellegi–Sunter model of record linkage, ERIC. Accessed 14 May 2017
  38. 38.
    Wu J, Chen L, Zheng Z, Lyu MR, Wu Z (2014) Clustering web services to facilitate service discovery. Knowl Inf Syst 38(1):207–229CrossRefGoogle Scholar
  39. 39.
    Wu P, Ho, SCH, Zhao, P, He Y (2011) Mining social images with distance metric learning for automated image tagging. In: 4th ACM international conference on Web search and data mining, pp 197–206Google Scholar
  40. 40.
    Zhou H, Zhang Z, Wu Y, Qian T (2011) Bio-inspired dynamic composition and reconfiguration of service-oriented internetware systems. Advances in swarm intelligence. Springer, Berlin, pp 364–373CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of CyprusNicosiaCyprus

Personalised recommendations