Characterizing Web APIs Combining Supervised Topic Model with Ontology
This paper presents a novel topic modeling (TM) approach for discovering meaningful topics for Web APIs, which is a potential dimensionality reduction way for efficient and effective classification, retrieval, organization, and management of numerous APIs. We exploit the possibility of conducting TM on multi-labeled APIs by combining a supervised TM (known as Labeled LDA) with ontology. Experiments conducting on real-world API data set show that the proposed method outperforms standard Labeled LDA with an average gain of 7.0% in measuring quality of the generated topics. In addition, we also evaluate the similarity matching between topics generated by our method and standard Labeled LDA, which demonstrates the significance of incorporating ontology.
web api supervised topic model ontology multi-labeled topic coherence