Research Area: Machine Learning
The process of Web service discovery identifies the most relevant services to requesters’ service queries. We propose a new measure of semantic similarity integrating multiple conceptual relationships (SIMCR) for Web service discovery. The new measure enables more accurate service-request comparison by treating different conceptual relationships in ontologies such as is-a, has-a and antonomy differently. Each service or request is represented by vectors of terms (or words) that characterize both the interface signature and textual description. The overall semantic similarity is computed as a weighted aggregation of interface similarity and description similarity. The experimental results confirm the effectiveness of the proposed semantic similarity measure. As demonstrated in this study, the semantic Web service discovery method based on the proposed similarity measure outperforms existing state-of-the-art discovery methods in terms of precision, recall and F-measure. The proposed semantic similarity measure has wider applications such as to improve document classification or clustering, and to more accurately represent and apply knowledge in expert and intelligent systems.
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Author(s) Name: Chen Fuzan, Lu Chenghua, Wu Harris, Li Minqiang
Journal name: Expert Systems with Applications
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Publisher name: Elsevier
DOI: 10.1016/j.eswa.2016.09.028
Volume Information: Volume 67, January 2017, Pages 19-31
