Semi-automatic terminology ontology learning based on topic modeling. (August 2017) - Rani, Monika; Dhar, Amit Kumar; Vyas, O.P.
Abstract: Ontologies provide features like a common vocabulary, reusability, machine-readable content, and also allows for semantic search, facilitate agent interaction and ordering & structuring of knowledge for the Semantic Web (Web 3.0) application. However, the challenge in ontology engineering is automatic learning, i.e., the there is still a lack of fully automatic approach from a text corpus or dataset of various topics to form ontology using machine learning techniques. In this paper, two topic modeling algorithms are explored, namely LSI & SVD and Mr.LDA for learning topic ontology. The objective is to determine the statistical relationship between document and terms to build a topic ontology and ontology graph with minimum human intervention. Experimental analysis on building a topic ontology and semantic retrieving corresponding topic ontology for the user's query demonstrating the effectiveness of the proposed approach. Highlights: LSI & SVD and Mr.LDA topic modeling are explored for Ontology Learning (OL). Automatic Text-to-Onto, NELL, OIE problems are resolved for OL using Mr.LDA. We used Fuzzy Co-clustering & Fuzzy Scale (Fuzzy Type 1 & 2) for semantic retrieval. Topic and words detection using ontologies for the semantic web applications. Ontology Similarity and ranking approach are used for experimental evaluation.