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 contributionpeer-review

4 Citations (Scopus)


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
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages4
ISBN (Electronic)9781538607527
ISBN (Print)9781538607534
Publication statusPublished - 2017
Externally publishedYes
Event24th IEEE International Conference on Web Services, ICWS 2017 - Honolulu, United States
Duration: 25 Jun 201730 Jun 2017


Conference24th IEEE International Conference on Web Services, ICWS 2017
Country/TerritoryUnited States


  • contextual similarity
  • service composition
  • word2vec


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