Gaussian LDA and Word Embedding for Semantic Sparse Web Service Discovery

  • Gang Tian
  • Jian WangEmail author
  • Ziqi Zhao
  • Junju Liu
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 201)


In recent years, more and more Web services are published in API marketplaces founded by cloud service providers or third party registries. In this situation, users rely heavily on the search engine model to retrieve their expected Web services. However, due to the fact that Web services registered in API marketplaces are described in short texts, the search engine based discovery method suffers from the semantic sparsity problem, which in turn leads to a poor recall during service discovery. To address this issue, in this paper, we propose a novel Web service discovery approach that uses Gaussian Latent Dirichlet Allocation (Gaussian LDA) and word embedding. More specifically, instead of clustering Web services like most existing service discovery approaches, we use word embedding to map the words as continuous word embeddings to extend and enrich the semantics of service descriptions. We also leverage the Gaussian LDA in service discovery, which takes continuous word distribution as the input and interprets the Web service description as a hierarchical model by its two distributions. Based on the Gaussian LDA and word embedding, we propose a Web service query and ranking approach. Experiments conducted on a real-world Web service dataset demonstrate the effectiveness of the proposed approach.


Word embedding Gaussian LDA Semantic sparsity Web service discovery 



The work is supported by the National Basic Research Program of China under grant No. 2014CB340404, the National Natural Science Foundation of China under grant Nos. 61672387 and 61373037, the State Key Laboratory of Software Engineering Foundation under the grant No.SKLSE 2014-10-07, University Science and Technology Program of Shandong Province under the grant No.J16LN08, Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents under the grant No.2016RCJJ045. Jian Wang is the corresponding author.


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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017

Authors and Affiliations

  1. 1.State Key Lab of Software EngineeringWuhan UniversityWuhanChina
  2. 2.College of Information Science and EngineeringShandong University of Science and TechnologyQingdaoChina
  3. 3.Zhixing CollegeHubei UniversityWuhanChina

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