Semi-automatic terminology ontology learning based on topic modeling. (August 2017)

• Record Type: Journal Article
• Authors: Rani, Monika; Dhar, Amit Kumar; Vyas, O.P.
• Is Part Of: Engineering applications of artificial intelligence. Volume 63(2017:Mar.)
• Page Start: 108
• Page End: 125
• Publication Date: 2017-08
• Subjects: Ontology Learning (OL) -- Latent Semantic Indexing (LSI) -- Singular Value Decomposition (SVD) -- Probabilistic Latent Semantic Indexing (pLSI) -- MapReduce Latent Dirichlet Allocation (Mr.LDA) -- Correlation Topic Modeling (CTM); Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2017.05.006
• Languages: English
• ISSNs: 0952-1976

Abstract

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.