Semantic Enrichment of Web Service Operations

  • Maricela BravoEmail author
  • José A. Reyes-Ortiz
  • Roberto Alcántara-Ramírez
  • Leonardo Sánchez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10022)


In this paper we describe the process by which semantic relatedness assertions are discovered and defined between Web service operations. The general approach relies on a global ontology model that describes Web services. Obtaining semantic similarities between operations is performed by calculating eight semantic relatedness measures between all operations pairs. The entire process consists of: Web service parsing, Web service data extraction, automatic Web service ontology population, similarity measures calculation, similarity discovery; and finally, object property assertion between web service operations.


Semantic web services Automatic discovery Ontology axioms 


  1. 1.
    Budanitsky, A., Hirst, G.: Semantic distance in WordNet: an experimental, application-oriented evaluation of five measures. In: Workshop on WordNet and Other Lexical Resources, vol. 2, p. 2, June 2001Google Scholar
  2. 2.
    Wu, Z., Palmer, M.: Verbs semantics and lexical selection. In: Proceedings of the 32nd Annual Meeting on Association for Computational Linguistics, pp. 133–138. Association for Computational Linguistics (1994)Google Scholar
  3. 3.
    Lin, D.: An information-theoretic definition of similarity. In: Proceedings of the 15th International Conference on Machine Learning, pp. 296– 304. Morgan Kaufmann, San Francisco (1998)Google Scholar
  4. 4.
    Leacock, C., Chodorow, M.: Combining local context and WordNet similarity for word sense identification. WordNet: Electron. Lex. Database 49(2), 265–283 (1998)Google Scholar
  5. 5.
    Banerjee, S., Pedersen, T.: An adapted Lesk algorithm for word sense disambiguation using WordNet. In: Gelbukh, A. (ed.) CICLing 2002. LNCS, vol. 2276, pp. 136–145. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  6. 6.
    Lesk, M.: Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from a ice cream cone. In: Proceedings of SIGDOC 1986 (1986)Google Scholar
  7. 7.
    Hirst, G., St-Onge, D.: Lexical chains as representations of context for the detection and correction of malapropisms. In: Fellbaum, C. (ed.) pp. 305–332 (1998)Google Scholar
  8. 8.
    Jiang, J.J., Conrath, D.W.: Semantic similarity based on corpus statistics and lexical taxonomy. In: Proceedings of International Conference on Research in Computational Linguistics, Taiwan (1997)Google Scholar
  9. 9.
    Resnik, P.: Using information content to evaluate semantic similarity. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, Montreal, pp. 448–453 (1995)Google Scholar
  10. 10.
    Dogac, A., Kabak, Y., Laleci, G.: Enriching ebXML registries with OWL ontologies for efficient service discovery. In: Proceedings of the 14th International Workshop on Research Issues on Data Engineering (RIDE 2004), pp. 69–76 (2004)Google Scholar
  11. 11.
    Paolucci, M., Kawamura, T., Payne, T.R., Sycara, K.: Semantic matching of web services capabilities. In: Horrocks, I., Hendler, J. (eds.) ISWC 2002. LNCS, vol. 2342, pp. 333–347. Springer, Heidelberrg (2002). doi: 10.1007/3-540-48005-6_26 CrossRefGoogle Scholar
  12. 12.
    Kourtesis, D., Paraskakis, I.: Combining SAWSDL, OWL-DL and UDDI for semantically enhanced web service Discovery. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds.) ESWC 2012. LNCS, vol. 7295, pp. 614–628. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-68234-9_45 CrossRefGoogle Scholar
  13. 13.
    Pilioura, T., Tsalgatidou, A.: Unified publication and discovery of semantic web services. ACM Trans. Web 3(3), 11–44 (2009)CrossRefGoogle Scholar
  14. 14.
    Lee, Y.-J., Kim, C.-S.: A learning ontology method for RESTful semantic web services. In: Proceedings of the IEEE International Conference on Web Services (2011)Google Scholar
  15. 15.
    Sabou, M., Pan, J.: Towards semantically enhanced Web service repositories. J. Web Semant.: Sci. Serv. Agents World Wide Web 5, 142–150 (2007)CrossRefGoogle Scholar
  16. 16.
    Linna, L.: The design of semantic web services discovery model based on multi proxy (2009)Google Scholar
  17. 17.
    Farrag, T., Saleh, A., Ali, H.: Mapping from traditional WS definitions into SWS Profile. In: 2nd International Conference on Computer Technology and Development (2010)Google Scholar
  18. 18.
    Pedrinaci, C., Liu, D., Maleshkova, M., Lambert, D., Kopecky, J., Domingue, J.: iServe: a linked services publishing platform. In: Paper Presented at the Workshop: Ontology Repositories and Editors for the Semantic Web at 7th Extended Semantic Web Conference, Heraklion, Crete (2010)Google Scholar
  19. 19.
    Yousefipour, A., Mohsenzadeh, M., Ghari Neiat, A., Sadegzadeh, M.: An ontology-based approach for ranking suggested semantic web services (2010)Google Scholar
  20. 20.
    Yu, J., Sheng, Q., Han, J., Wu, Y., Liu, C.: A semantically enhanced service repository for user-centric service discovery and management. Data and Knowl. Eng. 72, 202–218 (2011)CrossRefGoogle Scholar
  21. 21.
    Xu, B., Luo, S., Sun, K.: Towards multimodal query in web service search (2012)Google Scholar
  22. 22.
    Loutas, N., Peristeras, V., Zeginis, D., Tarabanis, K.: The Semantic Service Search Engine (S3E). J. Intell. Inf. Syst. 38, 645–668 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Maricela Bravo
    • 1
    Email author
  • José A. Reyes-Ortiz
    • 1
  • Roberto Alcántara-Ramírez
    • 1
  • Leonardo Sánchez
    • 1
  1. 1.Systems DepartmentAutonomous Metropolitan UniversityAzcapotzalco, DFMexico

Personalised recommendations