Clustering facilitated web services discovery model based on supervised term weighting and adaptive metric learning
Lei Chen, Geng Yang, Wei Hong Zhu, Yingzhou Zhang, Zhen Yang · International Journal of Web Engineering and Technology · 2013
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.