Semi-Automatic Terminology Ontology Learning Based on Topic Modeling
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.
Cite
@article{arxiv.1709.01991,
title = {Semi-Automatic Terminology Ontology Learning Based on Topic Modeling},
author = {Monika Rani and Amit Kumar Dhar and O. P. Vyas},
journal= {arXiv preprint arXiv:1709.01991},
year = {2017}
}