Information Systems Frontiers

, Volume 21, Issue 1, pp 175–189 | Cite as

Discovering composable web services using functional semantics and service dependencies based on natural language requests

  • Sowmya Kamath SEmail author
  • Ananthanarayana V. S.


The processes of service discovery, selection and composition are crucial tasks in web service based application development. Most web service-driven applications are complex and are composed of more than one service, so, it becomes important for application designers to identify the best service to perform the next task in the intended application’s workflow. In this paper, a framework for discovering composable service sets as per user’s complex requirements is proposed. The proposed approach uses natural language processing and semantics based techniques to extract the functional semantics of the service dataset and also to understand user context. In case of simple queries, basic services may be enough to satisfy the user request, however, in case of complex queries, several basic services may have to be identified to serve all the requirements, in the correct sequence. For this, the service dependencies of all the services are used for constructing a service interface graph for finding suitable composable services. Experiments showed that the proposed approach was effective towards finding relevant services for simple & complex queries and achieved an average accuracy rate of 75.09 % in finding correct composable service templates.


Web service discovery Service composition Natural language processing Semantics 


  1. Akkiraju, R., Srivastava, B., Ivan, A., Goodwin, R., & Syeda-Mahmood, T. (2006). Semantic matching to achieve web service discovery and composition, E-Commerce Technology, 2006. The 8th IEEE International Conference on and Enterprise Computing, E-Commerce, and E-Services (pp. 70–70).Google Scholar
  2. Almasri, E., & Mahmoud, Q.H. (2009). A broker for universal access to web services, Communication Networks and Services Research Conference, 2009. CNSR’09. Seventh Annual (pp. 118–125).CrossRefGoogle Scholar
  3. Benatallah, B., Hacid, M.S., Leger, A., Rey, C., & Toumani, F. (2005). On automating web services discovery. VLDB J., 14(1), 84– 96.CrossRefGoogle Scholar
  4. Bianchini, D., Garza, P., & Quintarelli, E. (2015). Characterization and search of web services through intensional knowledge. Journal of Intelligent Information Systems pp 1–27.Google Scholar
  5. Brogi, A., Corfini, S., & Popescu, R. (2005). Composition-oriented service discovery, Software Composition (pp. 15–30): Springer.Google Scholar
  6. Chan, N.N., Gaaloul, W., & Tata, S. (2011). A web service recommender system using vector space model and latent semantic indexing. In 2011 IEEE International Conference on Advanced Information Networking and Applications (AINA) (pp. 602– 609).Google Scholar
  7. Cuzzocrea, A., & Fisichella, M. (2011). Discovering semantic web services via advanced graph-based matching. In IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2011 (pp. 608–615).Google Scholar
  8. Della-Valle, E., Cerizza, D., Celino, I., Turati, A., Lausen, H., Steinmetz, N., Erdmann, M., & Funk, A. (2008). Realizing service-finder: Web service discovery at web scale. In European Semantic Technology Conference (ESTC), Vienna.Google Scholar
  9. D’Mello, D., Ananthanarayana, V., & Santhi, T. (2008). A qos broker based architecture for dynamic web service selection. In Modeling & Simulation, 2008. AICMS 08. Second Asia International Conference on (pp. 101–106).Google Scholar
  10. Dong, X., Halevy, A., Madhavan, J., Nemes, E., & Zhang, J. (2004). Similarity search for web services. In Proceedings of the Thirtieth international conference on Very large data bases-Volume 30, VLDB Endowment (pp. 372–383).Google Scholar
  11. Elgazzar, K., & et al. (2010). Clustering WSDL documents to bootstrap the discovery of web services. In IEEE Intl Conf on Web Services (ICWS), IEEE.Google Scholar
  12. Fang, L., Wang, L., Li, M., Zhao, J., Zou, Y., & Shao, L. (2012). Towards automatic tagging for web services. In 2012 IEEE 19th International Conference on Web Services (ICWS) (pp. 528–535).Google Scholar
  13. Feng, Z., Peng, R., Wong, R.K., He, K., Wang, J., Hu, S., & Li, B. (2013). Qos-aware and multi-granularity service composition. Infor. Syst. Front., 15(4), 553–567.CrossRefGoogle Scholar
  14. Fethallah, H., & Chikh, A. (2013a). Automated retrieval of semantic web services: a matching based on conceptual indexation. Int. Arab. J. Inf. Technol., 10(1), 61–66.Google Scholar
  15. Fethallah, H., & Chikh, A. (2013b). Automated retrieval of semantic web services: a matching based on conceptual indexation. Int. Arab. J. Inf. Technol., 10(1), 61–66.Google Scholar
  16. Hamadi, R., & Benatallah, B. (2003). A petri net-based model for web service composition. In Proceedings of the 14th Australasian database conference-Volume, (Vol. 17 pp. 191–200): Australian Computer Society, Inc.Google Scholar
  17. Hao, Y., Zhang, Y., & Cao, J. (2010). Web services discovery and rank: An information retrieval approach. Futur. Gener. Comput. Syst., 26(8), 1053–1062.CrossRefGoogle Scholar
  18. Hashemian, S., & Mavaddat, F. (2005). A graph-based approach to web services composition. In The 2005 Symposium on Applications and the Internet, 2005. Proceedings. doi: 10.1109/SAINT.2005.4 (pp. 183–189).
  19. Jiang, J.J., & Conrath, D.W. (1997). Semantic similarity based on corpus statistics and lexical taxonomy. arXiv:cmp-lg/9709008.
  20. Klusch, M., Fries, B., & Sycara, K. (2006). Automated semantic web service discovery with owls-mx. In Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (pp. 915–922).Google Scholar
  21. Kuang, L., Li, Y., Deng, S., & Wu, Z. (2007). Inverted indexing for composition-oriented service discovery. In IEEE International Conference on Web Services, 2007. ICWS 2007. (pp. 257– 264).Google Scholar
  22. Li, S., Huang, S.M., Yen, D.C., & Sun, J.C. (2013). Semantic-based transaction model for web service. Inf. Syst. Front., 15(2), 249–268.CrossRefGoogle Scholar
  23. Li, Y., Xiong, J., Liu, X., Zhang, H., & Zhang, P. (2014). Folksonomy-based in-depth annotation of web services. In 2014 IEEE 8th International Symposium on Service Oriented System Engineering (SOSE) (pp. 243–249).Google Scholar
  24. Liang, Q.A., & Su, S.Y. (2005). AND/OR graph and search algorithm for discovering composite web services. Int. J. Web. Ser. Res. (IJWSR), 2(4), 48–67.CrossRefGoogle Scholar
  25. Lin, M., & Cheung, D.W. (2014). Automatic tagging web services using machine learning techniques. In Proceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)-Volume 02 (pp. 258–265).Google Scholar
  26. Ma, J., Zhang, Y., & He, J. (2008). Web services discovery based on latent semantic approach. In IEEE International Conference on Web Services, 2008. ICWS’08. (pp. 740–747).Google Scholar
  27. Nayak, R., & Lee, B. (2007). Web service discovery with additional semantics and clustering.Google Scholar
  28. Oh, S.C., On, B.W., Larson, E., & Lee, D. (2005). Bf*: Web services discovery and composition as graph search problem. In The 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service, 2005. EEE ’05. Proceedings (pp. 784–786). doi: 10.1109/EEE.2005.41.
  29. Pedrinaci, C., Liu, D., Maleshkova, M., Lambert, D., Kopecky, J., & Domingue, J. (2010). iserve: a linked services publishing platform. In CEUR workshop proceedings, Vol. 596.Google Scholar
  30. Peng, Y., & Wu, C. (2010). Automatic semantic web service discovery based on assignment algorithm. In 2010 2nd International Conference on Computer Engineering and Technology, Vol. 6.Google Scholar
  31. Platzer, C., & Dustdar, S. (2005). A vector space search engine for web services. Third European Conference on Web Services DOI 0-7695-2484-2/05.Google Scholar
  32. Rong, W., Peng, B., Ouyang, Y., Liu, K., & Xiong, Z. (2015). Collaborative personal profiling for web service ranking and recommendation. Inf. Syst. Front., 17(6), 1265–1282.CrossRefGoogle Scholar
  33. Sangers, J., Frasincar, F., Hogenboom, F., & Chepegin, V. (2013). Semantic web service discovery using natural language processing techniques. Expert Syst. Appl., 40(11), 4660–4671.CrossRefGoogle Scholar
  34. Shiaa, M.M., Fladmark, J.O., & Thiell, B. (2008). An incremental graph-based approach to automatic service composition. In IEEE International Conference on Services Computing, 2008. SCC’08. (Vol. 1 pp. 397–404).Google Scholar
  35. Song, H., Cheng, D., Messer, A., & Kalasapur, S. (2007). Web service discovery using general-purpose search engines. In IEEE International Conference on Web Services, 2007. ICWS 2007. (pp. 265–271).Google Scholar
  36. Steinmetz, N., Lausen, H., & Brunner, M. (2009). Web service search on large scale. In Service-Oriented Computing (pp. 437–444): Springer.Google Scholar
  37. Truong, H.L., & Dustdar, S. (2009). A survey on context aware web service systems. Int. J. Web Infor. Syst., 5(1), 5–31. doi: 10.1108/17440080910947295.CrossRefGoogle Scholar
  38. Varguez-Moo, M., Moo-Mena, F., & Uc-Cetina, V. (2013). Use of classification algorithms for semantic web services discovery. J. Comput., 8(7), 1810–1814.CrossRefGoogle Scholar
  39. Wohed, P., van der Aalst, W.M., Dumas, M., & Ter Hofstede, A.H. (2003). Analysis of web services composition languages: The case of bpel4ws. In Conceptual Modeling-ER 2003 (pp. 200–215): Springer.Google Scholar
  40. Wu, C., & Chang, E. (2008). Searching services on the web- a public web services discovery approach. In Third Conf. on Signal-Image Technologies and Internet based Systems, IEEE.Google Scholar
  41. Wu, C., & et al. (2008). An empirical approach for semantic web services discovery. In 19th Australian Conference on Software Engineering, 2008. ASWEC 2008. (pp. 412–421).Google Scholar
  42. Xie, X., Chen, K., & Li, J. (2006). A composition oriented and graph-based service search method. In The Semantic Web–ASWC 2006 (pp. 530–536): Springer.Google Scholar
  43. Yang, J., & Zhou, X. (2014). Semi-automatic algorithm based on web service classification. In Advanced Science and Technology Letters, (Vol. 53 pp. 88–91).Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Information TechnologyNational Institute of Technology KarnatakaSurathkal, MangaloreIndia

Personalised recommendations