Incorporating LDA With Word Embedding for Web Service Clustering

Yi Zhao, Chong Wang, Jian Wang and Keqing He
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Yi Zhao: School of Computer Science, Wuhan University, Wuhan, China
Chong Wang: School of Computer Science, Wuhan University, Wuhan, China
Jian Wang: School of Computer Science, Wuhan University, Wuhan, China
Keqing He: School of Computer Science, Wuhan University, Wuhan, China

International Journal of Web Services Research (IJWSR), 2018, vol. 15, issue 4, 29-44

Abstract: With the rapid growth of web services on the internet, web service discovery has become a hot topic in services computing. Faced with the heterogeneous and unstructured service descriptions, many service clustering approaches have been proposed to promote web service discovery, and many other approaches leveraged auxiliary features to enhance the classical LDA model to achieve better clustering performance. However, these extended LDA approaches still have limitations in processing data sparsity and noise words. This article proposes a novel web service clustering approach by incorporating LDA with word embedding, which leverages relevant words obtained based on word embedding to improve the performance of web service clustering. Especially, the semantically relevant words of service keywords by Word2vec were used to train the word embeddings and then incorporated into the LDA training process. Finally, experiments conducted on a real-world dataset published on ProgrammableWeb show that the authors' proposed approach can achieve better clustering performance than several classical approaches.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jwsr00:v:15:y:2018:i:4:p:29-44

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