bi-HPTM: An Effective Semantic Matchmaking Model for Web Service Discovery

Analyzing textual semantics in matching user query and service description is critical for Web service discovery. Existing works mostly extract the features of the description and query independently, downgrading them into word-level calculation, which can not jointly extract the accurate semantics. For this issue, this work explores a way to enable the semantic matching for the contents (including words, phrases, or sentences) in query and the sentences in service description, by mapping words and sentences into the same semantic space. Specifically, we propose an unsupervised Bayesian probabilistic model, bi-Directional Hybrid Priors Topic Model (bi-HPTM), to capture the textual semantics of the words and sentences in a probabilistic simplex, which provides a flexible operation to build the semantic links from the queries to service descriptions. Meanwhile, the textual semantics generated by bi-HPTM is highly interpretable that help to understand the user requirements. The proposed model is examined by ProgrammableWeb. Experimental results demonstrate that bi-HPTM outperforms state-of-the-art methods for semantic service discovery on service classification and retrieval. The visualizations of the nearest-neighbored queries and descriptions show the insights of our model on capturing the latent semantics of Web services.