scholarly journals Semantic Service Clustering With Lightweight BERT-Based Service Embedding Using Invocation Sequences

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 54298-54309
Author(s):  
Kungan Zeng ◽  
Incheon Paik
2018 ◽  
Vol 15 (4) ◽  
pp. 29-44 ◽  
Author(s):  
Yi Zhao ◽  
Chong Wang ◽  
Jian Wang ◽  
Keqing He

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.


2006 ◽  
Vol 12 (2) ◽  
pp. 107-119 ◽  
Author(s):  
Thorsten Möller ◽  
Heiko Schuldt ◽  
Andreas Gerber ◽  
Matthias Klusch

2007 ◽  
pp. 221-230 ◽  
Author(s):  
Stanislav Pokraev ◽  
Dick Quartel ◽  
Maarten W. A. Steen ◽  
Manfred Reichert

2010 ◽  
Vol 13 (1-2) ◽  
pp. 75-103 ◽  
Author(s):  
Hai Dong ◽  
Farookh Khadeer Hussain ◽  
Elizabeth Chang

Author(s):  
Supannada Chotipant ◽  
Farookh Khadeer Hussain ◽  
Hai Dong ◽  
Omar Khadeer Hussain
Keyword(s):  

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