# Chatbot-Based Ontology Interaction Using Large Language Models and Domain-Specific Standards

arXiv: https://arxiv.org/abs/2408.00800 · HTML: https://mlsanity.com/paper/2408.00800/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 15 references that are in the map.
- First posted on arXiv 22 Jul 2024.

## Builds on (most cited first, 1 of 1)

- [OWL2Vec*: Embedding of OWL Ontologies](https://mlsanity.com/paper/2009.14654/) (2020)

## Built on by (most cited first, 2 of 2)

- [From Detection to Action: Using LLM Agents for Fault-Tolerant Control](https://mlsanity.com/paper/2606.28011/) (2026)
- [Consistency Verification in Ontology-Based Process Models with Parameter Interdependencies](https://mlsanity.com/paper/2506.16087/) (2025)

## Abstract (arXiv)

The following contribution introduces a concept that employs Large Language Models (LLMs) and a chatbot interface to enhance SPARQL query generation for ontologies, thereby facilitating intuitive access to formalized knowledge. Utilizing natural language inputs, the system converts user inquiries into accurate SPARQL queries that strictly query the factual content of the ontology, effectively preventing misinformation or fabrication by the LLM. To enhance the quality and precision of outcomes, additional textual information from established domain-specific standards is integrated into the ontology for precise descriptions of its concepts and relationships. An experimental study assesses the accuracy of generated SPARQL queries, revealing significant benefits of using LLMs for querying ontologies and highlighting areas for future research.