Relationship network augmented web services clustering

Yingcheng Cao, Jianxun Liu, Min Shi, Buqing Cao, Xiangping Zhang, Yan Wang

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

14 Citations (Scopus)


Clustering Web services can promote the quality of services discovery and management within a service repository. Traditional clustering methods primarily focus on using the semantic distance between service features, i.e., latent topics learned from WSDL documents, to measure the service content similarity between Web services. Few works exploited the structural information generated during the usage of Web services, i.e., the service compositing and tagging behaviors. Nowadays, Web services frequently interact (e.g., composition relation and tag sharing relation) with each other to form a complex service relationship network. The rich network relations inherently reflect either positive or negative categorical relevance between services, which can be strong supplement of service semantics in characterizing the functional affinities between services. In this paper, we propose to utilize the services relationship network for augmented services clustering algorithm design. We first learn semantic information from service descriptions based on the widely used Doc2Vec model. Then, we propose a revised K-means algorithm for service clustering that benefits simultaneously from service semantics and network relations, where the service relations are previously preserved in a set of low-dimensional vectors achieved based on a recently proposed network embedding technique. Experiments on a real-world dataset demonstrated that the proposed clustering approach yields an improvement of 6.89% than the state-of-the-art.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Web Services
EditorsElisa Bertino, Carl K. Chang, Peter Chen, Ernesto Damiani, Ernesto Damiani, Michael Goul, Katsunori Oyama
Place of PublicationLos Alamitos, California
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Electronic)9781728127170
Publication statusPublished - 2019
Event2019 IEEE International Conference on Web Services - Milan, Italy
Duration: 8 Jul 201913 Jul 2019


Conference2019 IEEE International Conference on Web Services


  • Web services
  • Services clustering
  • Services network
  • Network embedding
  • Semantic mining


Dive into the research topics of 'Relationship network augmented web services clustering'. Together they form a unique fingerprint.

Cite this