A Web service search engine for large-scale Web service discovery based on the probabilistic topic modeling and clustering

With the ever increasing number of Web services, discovering an appropriate Web service requested by users has become a vital yet challenging task. We need a scalable and efficient search engine to deal with the large volume of Web services. The aim of this approach is to provide an efficient search...

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Veröffentlicht in:Service oriented computing and applications Jg. 12; H. 2; S. 169 - 182
Hauptverfasser: Bukhari, Afnan, Liu, Xumin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer London 01.06.2018
Springer Nature B.V
Schlagworte:
Clustering
Computer Appl. in Administrative Data Processing
Computer Science
Computer Systems Organization and Communication Networks
Descriptions
Dirichlet problem
e-Commerce/e-business
Internet service providers
IT in Business
Keywords
Management of Computing and Information Systems
Modelling
Original Research Paper
Search engines
Searching
Semantics
Software Engineering/Programming and Operating Systems
Web services
Discovery
Clustering
Vector
Topic model
Web service
ISSN:1863-2386, 1863-2394
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Zusammenfassung:With the ever increasing number of Web services, discovering an appropriate Web service requested by users has become a vital yet challenging task. We need a scalable and efficient search engine to deal with the large volume of Web services. The aim of this approach is to provide an efficient search engine that can retrieve the most relevant Web services in a short time. The proposed Web service search engine (WSSE) is based on the probabilistic topic modeling and clustering techniques that are integrated to support each other by discovering the semantic meaning of Web services and reducing the search space. The latent Dirichlet allocation (LDA) is used to extract topics from Web service descriptions. These topics are used to group similar Web services together. Each Web service description is represented as a topic vector, so the topic model is an efficient technique to reduce the dimensionality of word vectors and to discover the semantic meaning that is hidden in Web service descriptions. Also, the Web service description is represented as a word vector to address the drawbacks of the keyword-based search system. The accuracy of the proposed WSSE is compared with the keyword-based search system. Also, the precision and recall metrics are used to evaluate the performance of the proposed approach and the keyword-based search system. The results show that the proposed WSSE based on LDA and clustering outperforms the keyword-based search system.
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ISSN:1863-2386
1863-2394
DOI:10.1007/s11761-018-0232-6