Towards Rules-Based Mapping Framework for RESTful Web Services

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10380)


Integrating web services is usually time-consuming and requires a lot of programming efforts and experiences due to documentation burden and coding style convention overhead provided by external parties. Fortunately, RESTful web services simplify the integration task compared to the traditional web service WSDL as coding convention is significantly reduced. However, the documentation burden is still evident and developers usually rely on running examples to gain better knowledge and use web service documentation efficiently. The subject of my PhD thesis is to propose a novel rules-based mapping method for mapping a desired web service to potential candidates from a predefined web service repository. In this research, the assumption is that web services are made of RESTful APIs specified in Javascript Object Notation (JSON) format. My significant thesis contributions are: (1) a hybrid model for web service similarity, (2) a rules-based mapping approach for identifying and classifying the most related and similar services against a given desired web service, and lastly (3) a concrete and detailed evaluation to show the effectiveness of the proposed approach and framework.


Web services RESTful APIs Web service matching and mapping Web service programming 


  1. 1.
    Walsh, A.E. (ed.): UDDI, SOAP, and WSDL: The Web Services Specification Reference Book. Prentice Hall Professional Technical Reference, Englewood Cliffs (2002)Google Scholar
  2. 2.
    Richardson, L., Ruby, S. (eds.): RESTful Web Services. O’Reilly Media, Sebastopol (2007)Google Scholar
  3. 3.
    JSON: Javascript object notation. Accessed 09 Jan 2016
  4. 4.
    Swagger: A simple, open standard for describing REST APIs with JSON (2016). Accessed 10 Jan 2016
  5. 5.
    Dong, X., Halevy, A., Madhavan, J., Nemes, E., Zhang, J.: Similarity search for web services. In: Proceedings of the Thirtieth International Conference on Very Large Data Bases (VLDB 2004), vol. 30. pp. 372–383. VLDB Endowment (2004).
  6. 6.
    Fellbaum, C. (ed.): WordNet: An Electronic Lexical Database. Language, Speech, and Communication. MIT Press, Cambridge (1998)MATHGoogle Scholar
  7. 7.
    Rahm, E., Bernstein, P.A.: A survey of approaches to automatic schema matching. VLDB J. 10(4), 334–350 (2011). CrossRefMATHGoogle Scholar
  8. 8.
    Islam, A., Inkpen, D., Kiringa, I.: Database schema matching using corpus-based semantic similarity and word segmentationGoogle Scholar
  9. 9.
    Doan, A., Halevy, A.Y.: Semantic-integration research in the database community. AI Mag. 26(1), 83–94 (2005). Google Scholar
  10. 10.
    Do, H.H., Rahm, E.: COMA: a system for flexible combination of schema matching approaches. In: Proceedings of the 28th International Conference on Very Large Data Bases (VLDB 2002), pp. 610–621. VLDB Endowment (2002).
  11. 11.
    Aumueller, D., Do, H.H., Massmann, S., Rahm, E.: Schema and ontology matching with COMA++. In: Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data (SIGMOD 2005), New York, pp. 906–908 (2005).
  12. 12.
    Engmann, D., Massmann, S.: Instance matching with COMA++. In: BTW (2007)Google Scholar
  13. 13.
    Cohen, W.W., Ravikumar, P., Fienberg, S.E.: Second string: an opensource Java toolkit of approximate string-matching techniques (2003).
  14. 14.
    Cohen, W.W., Ravikumar, P., Fienberg, S.E.: A comparison of string distance metrics for name-matching tasks, pp. 73–78 (2003)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.The School of Computer Science and EngineeringUniversity of New South WalesSydneyAustralia

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