Open AccessA semantic web services discovery approach integrating multiple similarity measures and k-means clustering
Author(s) -
Mourad Fariss,
Naoufal El Allali,
Hakima Asaidi,
Mohamed Bellouki
Publication year - 2021
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v24.i2.pp1228-1237
Subject(s) - computer science , web service , social semantic web , service discovery , cluster analysis , semantic similarity , information retrieval , data web , task (project management) , semantic web stack , similarity (geometry) , web modeling , service (business) , service oriented architecture , data mining , world wide web , semantic web , artificial intelligence , engineering , economy , systems engineering , economics , image (mathematics)
Web service (WS) discovery is an essential task for implementing complex applications in a service oriented architecture (SOA), such as selecting, composing, and providing services. This task is limited semantically in the incorporation of the customer’s request and the web services. Furthermore, applying suitable similarity methods for the increasing number of WSs is more relevant for efficient web service discovery. To overcome these limitations, we propose a new approach for web service discovery integrating multiple similarity measures and k-means clustering. The approach enables more accurate services appropriate to the customer's request by calculating different similarity scores between the customer's request and the web services. The global semantic similarity is determined by applying k-means clustering using the obtained similarity scores. The experimental results demonstrated that the proposed semantic web service discovery approach outperforms the state-of-the approaches in terms of precision (98%), recall (95%), and F-measure (96%). The proposed approach is efficiently designed to support and facilitate the selection and composition of web services phases in complex applications.