Clustering facilitated web services discovery model based on supervised term weighting and adaptive metric learning

Abstract

With the explosive growth of web services, the research on how to rapidly find the desired services becomes increasingly important and challenging. In this paper, we focus on non-semantic web services discovery and present an efficient clustering facilitated web services discovery model CFWSFinder. Compared with the existing models, CFWSFinder has several characteristics. First, in services representing process, CFWSFinder imports WordNet and latent semantics index to represent non-semantic web services as the low-dimensional compact semantic feature vectors; Second, in services clustering process, CFWSFinder employs a modified Kernel batch self-organising map KBSOM neural network to minimise the services discovery duration; Third and most importantly, in services matching process, by using the category label information achieved from services clustering process, CFWSFinder can adopt supervised term weighting scheme and adaptive metric learning method to ameliorate the services discovery precision. Finally, experimental results performed on the real-world web services collection demonstrate the feasibility of the CFWSFinder.