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

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  • Sowmya Kamath S

    (National Institute of Technology Karnataka)

  • Ananthanarayana V. S.

    (National Institute of Technology Karnataka)

Abstract

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.

Suggested Citation

  • Sowmya Kamath S & Ananthanarayana V. S., 2019. "Discovering composable web services using functional semantics and service dependencies based on natural language requests," Information Systems Frontiers, Springer, vol. 21(1), pages 175-189, February.
    • Handle: RePEc:spr:infosf:v:21:y:2019:i:1:d:10.1007_s10796-017-9738-2
    DOI: 10.1007/s10796-017-9738-2
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    References listed on IDEAS

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    1. Wenge Rong & Baolin Peng & Yuanxin Ouyang & Kecheng Liu & Zhang Xiong, 2015. "Collaborative personal profiling for web service ranking and recommendation," Information Systems Frontiers, Springer, vol. 17(6), pages 1265-1282, December.
    2. Shing-Han Li & Shi-Ming Huang & David C. Yen & Jui-Chang Sun, 2013. "Semantic-based transaction model for web service," Information Systems Frontiers, Springer, vol. 15(2), pages 249-268, April.
    3. Qianhui Althea Liang & Stanley Y.W. Su, 2005. "AND/OR Graph and Search Algorithm for Discovering Composite Web Services," International Journal of Web Services Research (IJWSR), IGI Global, vol. 2(4), pages 48-67, October.
    4. Zaiwen Feng & Rong Peng & Raymond K. Wong & Keqing He & Jian Wang & Songlin Hu & Bing Li, 2013. "QoS-aware and multi-granularity service composition," Information Systems Frontiers, Springer, vol. 15(4), pages 553-567, September.
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