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

TL;DR

This paper presents a chatbot system leveraging Large Language Models and domain standards to generate accurate SPARQL queries from natural language, improving ontology access and reducing misinformation.

Contribution

It introduces a novel approach combining LLMs and domain standards for precise, natural language-based ontology querying with a chatbot interface.

Findings

LLMs significantly improve SPARQL query accuracy

Integration of domain standards enhances query precision

System effectively prevents misinformation in ontology queries

Abstract

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.