Ontologies provide features like a common vocabulary, reusability,machine-readable content, and also allows for semantic search, facilitate agentinteraction and ordering & structuring of knowledge for the Semantic Web (Web3.0) application. However, the challenge in ontology engineering is automaticlearning, i.e., the there is still a lack of fully automatic approach from atext corpus or dataset of various topics to form ontology using machinelearning techniques. In this paper, two topic modeling algorithms are explored,namely LSI & SVD and Mr.LDA for learning topic ontology. The objective is todetermine the statistical relationship between document and terms to build atopic ontology and ontology graph with minimum human intervention. Experimentalanalysis on building a topic ontology and semantic retrieving correspondingtopic ontology for the user's query demonstrating the effectiveness of theproposed approach.
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