Integrating semantic similarity with Dirichlet multinomial mixture model for enhanced web service clustering

With accelerated advancement of web 2.0, developers generally describe the functionality of services in short natural text. Keyword-based searching techniques are not an efficient way of discovering services from repositories. It suffers from vocabulary problems. Latent Dirichlet allocation (LDA) with word embedding techniques is widely adopted for efficiently extracting latent features from the service descriptions. However, LDA is not efficient on short text due to limited content and inadequate occurring words. The word vectors generated by word embedding techniques are of finer quality than topic modeling techniques. Gibbs sampling algorithm for Dirichlet multinomial mixture (GSDMM) model gives better results on web service description documents because it provides one topic corresponding to one document. In this paper, we evaluate the performance of GSDMM model with word embeddings and propose WV+GSDMMK model. The proposed model improves service-to-topic mapping by determining semantic similarity among features. K-means clustering is applied on service to topic representation. Results are evaluated on five real-time datasets based on intrinsic and extrinsic evaluation measures. Experimental results demonstrate that the proposed method outperforms other baseline techniques, and the accuracy score is also increased by 5%, 18%, 3%, 4%, and 6% on datasets DS1, DS2, DS3, DS4, and DS5, respectively.