Semi-Automatic Terminology Ontology Learning Based on Topic Modeling
This work addresses the problem of automating ontology engineering for Semantic Web applications, but it is incremental as it builds on existing topic modeling methods.
The paper tackled the challenge of automatic ontology learning from text corpora by exploring LSI & SVD and Mr.LDA topic modeling algorithms to build topic ontologies with minimal human intervention, demonstrating effectiveness in experimental analysis for semantic retrieval.
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.