Paper Info
Title
Context-Aware Web Service Clustering And Visualization
Abstract
With the large number of web services now available via the internet, web service discovery has become a challenging and time-consuming task. Organizing web services into similar clusters is a very efficient approach to reducing the search space. A principal issue for clustering is computing the semantic similarity between services. Current approaches do not consider the domain-specific context in measuring similarity and this has affected their clustering performance. This paper proposes a context-aware similarity (CAS) method that learns domain context by machine learning to produce models of context for terms retrieved from the web. To analyze visually the effect of domain context on the clustering results, the clustering approach applies a spherical associated-keyword-space algorithm. The CAS method analyzes the hidden semantics of services within a particular domain, and the awareness of service context helps to find cluster tensors that characterize the cluster elements. Experimental results show that the clustering approach works efficiently.
Year
DOI
Venue
2020
10.4018/IJWSR.2020100103
INTERNATIONAL JOURNAL OF WEB SERVICES RESEARCH
Keywords
DocType
Volume
Cluster Visualization, Context-Awareness, Web Service Clustering, Web Service Discovery, Web Services
Journal
17
Issue
ISSN
Citations 
4
1545-7362
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Banage T. G. S. Kumara1629.65
Incheon Paik224138.80
Yuichi Yaguchi34016.52