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Socially-Enriched Semantic Mashup of Web APIs

  • Jooik Jung
  • Kyong-Ho Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7636)

Abstract

As Web mashups are becoming one of the salient tools for providing composite services that satisfy users’ requests, there have been many endeavors to enhance the process of recommending the most adequate mashup to users. However, previous approaches show numerous pitfalls such as the problem of cold-start, and the lack of utilization of social information as well as functional properties of Web APIs and mashups. All these factors undoubtedly hinder the proliferation of mashup users as locating the most appropriate mashup becomes a cumbersome task. In order to resolve the issues, we propose an efficient method of recommending mashups based on the functional and social features of Web APIs. Specifically, the proposed method utilizes the social and functional relationships among Web APIs to produce and recommend the chains of candidate mashups. Experimental results with a real world data set show a precision of 86.9% and a recall of 75.2% on average, which validates that the proposed method performs more efficiently for various kinds of user requests as compared to a previous work.

Keywords

Web api mashup recommendation functional semantics social relationship 

References

  1. 1.
    Elmeleegy, H., Ivan, A., Akkiraju, R., Goodwin, R.: Mashup advisor: a recommendation tool for Mashup development. In: IEEE International Conference on Web Services, ICWS, pp. 337–344 (2008)Google Scholar
  2. 2.
    Tapia, B., Torres, R., Astudillo, H.: Simplifying mahsup component selection with a combined similarity- and social-based technique. In: 5th International Workshop on Web APIs and Service Mashups, MASHUPS (2011)Google Scholar
  3. 3.
    Torres, R., Tapia, B., Astudillo, H.: Improving Web API Discovery by leveraging social information. In: IEEE International Conference on Web Services, ICWS, pp. 744–745 (2011)Google Scholar
  4. 4.
    Shin, D.H., Lee, K.-H., Suda, T.: Automated generation of composite web services based on functional semantics. Journal of Web Semantics 7(4), 332–343 (2009)CrossRefGoogle Scholar
  5. 5.
    Maximilien, E.M., Wilkinson, H., Desai, N., Tai, S.: A Domain-Specific Language for Web APIs and Services Mashups. In: Krämer, B.J., Lin, K.-J., Narasimhan, P. (eds.) ICSOC 2007. LNCS, vol. 4749, pp. 13–26. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Goarany, K., Kulczycki, G., Blake, M.B.: Mining social tags to predict mashup patterns. In: 2nd International Workshop on Search and Mining User-Generated Contents, SMUC, pp. 71–78 (2010)Google Scholar
  7. 7.
    Bouillet, E., Feblowitz, M., Feng, H., Liu, Z., Ranganathan, A., Riabov, A.: A folksonomy-based model of web services for discovery and automatic composition. In: IEEE International Conference on Services Computing, SCC, pp. 389–396 (2008)Google Scholar
  8. 8.
    Fernandez, A., Hayes, C., Loutas, N., Peristeras, V., Polleres, A., Tarabanis, K.: Closing the Service Discovery Gap by Collaborative Tagging and Clustering Techniques. In: 7th International Semantic Web Conference, ISWC, pp. 115–128 (2008)Google Scholar
  9. 9.
    Gomadam, K., Ranabahu, A., Nagarajan, M., Sheth, A.P., Verma, K.: A faceted classification based approach to search and rank web apis. In: IEEE International Conference on Web Services, ICWS, pp. 177–184 (2008)Google Scholar
  10. 10.
    Schall, D., Truong, H.L., Dustdar, S.: Unifying Human and Software Serivces in Web-Scale Collaborations. IEEE Internet Computing 12(3), 62–68 (2008)CrossRefGoogle Scholar
  11. 11.
    Maaradji, A., Hacid, H., Daigremont, J.: Towards a Social Network Based Approach for Services Composition. In: IEEE International Conference on Communications, ICC, pp. 1–5 (2010)Google Scholar
  12. 12.
    Maaradji, A., Hacid, H., Skraba, R.: Social Web Mashups Full Completion via Frequent Sequence Mining. In: IEEE World Congress on Services, SERVICES, pp. 9–16 (2011)Google Scholar
  13. 13.
    Maaradji, A., Hacid, H., Skraba, R., Lateef, A., Daigremont, J., Crespi, N.: Social-based Web services discovery and composition for step-by-step mashup completion. In: IEEE International Conference on Web Services, ICWS, pp. 700–701 (2011)Google Scholar
  14. 14.
    Yu, S., Woodard, C.J.: Innovation in the Programmable Web: Characterizing the Mashup Ecosystem. In: Feuerlicht, G., Lamersdorf, W. (eds.) ICSOC 2008. LNCS, vol. 5472, pp. 136–147. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  15. 15.
    Wang, J., Chen, H., Zhang, Y.: Mining user behavior pattern in mashup community. In: 10th IEEE International Conference on Information Reuse & Integration, IRI, pp. 126–131 (2009)Google Scholar
  16. 16.
    Lim, J.H., Lee, K.-H.: Constructing composite web services from natural language requests. Journal of Web Semantics 8(1), 1–13 (2010)CrossRefGoogle Scholar
  17. 17.
    Briscoe, T., Carroll, J., Watson, R.: The second release of the RASP system. In: Proc. of the COLING/ACL Conference, pp. 77–80 (2006)Google Scholar
  18. 18.
    Li, Y., Bandar, Z.A., McLean, D.: An Approach for Measuring Semantic Similarity between Words Using Multiple Information Sources. IEEE Transactions on Knowledge and Data Engineering 15(4), 871–882 (2003)CrossRefGoogle Scholar
  19. 19.
    Klusch, M., Fries, B., Sycara, K.: OWLS-MX: A hybrid Semantic Web service matchmaker for OWL-S services. Journal of Web Semantics 7(2), 121–133 (2009)CrossRefGoogle Scholar
  20. 20.
    Roy Chowdhury, S., Daniel, F., Casati, F.: Efficient, Interactive Recommendation of Mashup Composition Knowledge. In: Kappel, G., Maamar, Z., Motahari-Nezhad, H.R. (eds.) ICSOC 2011. LNCS, vol. 7084, pp. 374–388. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  21. 21.
    Melchiori, M.: Hybrid Techniques for Web APIs Recommendation. In: 1st International Workshop on Linked Web Data Management, LWDM, pp. 17–23 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jooik Jung
    • 1
  • Kyong-Ho Lee
    • 1
  1. 1.Department of Computer ScienceYonsei UniversitySeoulRepublic of Korea

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