Semi-Automatic Terminology Ontology Learning Based on Topic Modeling

TL;DR

This paper explores semi-automatic methods for building ontologies from text using topic modeling algorithms, aiming to reduce human effort and improve semantic retrieval.

Contribution

It introduces a novel approach combining LSI, SVD, and Mr.LDA for automatic topic ontology learning from diverse datasets.

Findings

Effective ontology construction demonstrated through experiments

Improved semantic retrieval accuracy

Reduced human intervention in ontology engineering

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.