Semantic Enhancement and Heterogeneous Correlation Guided Web Service Clustering

Abstract

Service description suffers from short texts and contains few repeated words, which brings challenges to generate high-quality service function vector (SFV) in service clustering. Some works introduce service association to improve service clustering quality. However, they simply introduce single associations, such as tag associations or collaboration association. Single service association can only improve the clustering quality from one perspective of positive or negative categorical relevance. In this study, we propose semantic enhancement and heterogeneous correlation guided Web Service Clustering. A high-performance contrastive learning framework is employed to generate SFVs. Meanwhile, we propose a method for the semantic enhancement of SFVs by obtaining twin service descriptions through verb substitution. A heterogeneous association is established based on tag association and collaboration association. It quantitatively enhances the clustering quality from both positive and negative categorical relevance. Experiments show that the proposed method outperforms popular semantic enhancement ways in generating high-quality SFVs. The heterogeneous association can significantly improve service clustering quality compared to single tag association or collaborative association. The clustering quality obtained by our method is improved by 13.7\%, 9\%, 6.8\%, 6.1\%, and 5.5\% on average over the state-of-the-art service clustering methods in terms of DBI, SC, AMI, NMI, and Purity.