# Semi-Automatic Terminology Ontology Learning Based on Topic Modeling

arXiv: https://arxiv.org/abs/1709.01991 · HTML: https://mlsanity.com/paper/1709.01991/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2017.

## Built on by (most cited first, 3 of 3)

- [Combining Machine Learning and Ontology: A Systematic Literature Review](https://mlsanity.com/paper/2401.07744/) (2024)
- [Knowledge Triggering, Extraction and Storage via Human-Robot Verbal Interaction](https://mlsanity.com/paper/2104.11170/) (2021)
- [From Prompt to Graph: Comparing LLM-Based Information Extraction Strategies in Domain-Specific Ontology Development](https://mlsanity.com/paper/2602.00699/) (2026)

## Abstract (arXiv)

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.