A Web Service Clustering Method Based on Semantic Similarity and Multidimensional Scaling Analysis

Abstract

Clustering web services is an effective method to solving service computing problems. The key insight behind it is to extract the vectors based on the service description documents. However, the brevity of natural language service description documents typically complicates the vector construction process. To circumvent the difficulty, we propose a novel web service clustering method to vectorize documents based on the semantic similarity, which can be calculated via WordNet and multidimensional scaling (WMS) analysis. We utilize the dataset from the ProgrammableWeb to conduct extensive experiments and achieve prominent advances in precision, recall, and F-measure.