Service2vec

a vector representation for web services

Yuqun Zhang, Mengshi Zhang, Xi Zheng, Dewayne E. Perry

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

1 Citation (Scopus)

Abstract

Among the approaches that investigate the similarity between web services, hardly any concentrates on the impacts from contexts. In this paper we introduce service2vec which is an approach to represent web services as service embeddings based on a recent popular deep learning technique word2vec. Our approach composes and combines web services to be a document that is trained by the modeling technique of word2vec. As a result, each web service in the document is vectorized. By taking the advantage of word2vec, the resulting service embeddings of service2vec can be used to illustrate the contextual relations between web services. The experimental results suggest that service2vec can deliver contextual similarity between web services.
Original languageEnglish
Title of host publicationICWS 2017
Subtitle of host publicationProceedings of 2017 IEEE 24th International Conference on Web Services
EditorsShiping Chen, Ilkay Altintas
Place of PublicationPiscataway, NJ
Pages890-893
Number of pages4
ISBN (Electronic)9781538607527
DOIs
Publication statusPublished - 2017
Externally publishedYes

Keywords

  • contextual similarity
  • service composition
  • word2vec

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  • Cite this

    Zhang, Y., Zhang, M., Zheng, X., & Perry, D. E. (2017). Service2vec: a vector representation for web services. In S. Chen, & I. Altintas (Eds.), ICWS 2017: Proceedings of 2017 IEEE 24th International Conference on Web Services (pp. 890-893). Piscataway, NJ. https://doi.org/10.1109/ICWS.2017.114