# OntoChat: a Framework for Conversational Ontology Engineering using Language Models

arXiv: https://arxiv.org/abs/2403.05921 · HTML: https://mlsanity.com/paper/2403.05921/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 33 references that are in the map.
- First posted on arXiv 9 Mar 2024.

## Builds on (most cited first, 16 of 16)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild](https://mlsanity.com/paper/1906.02569/) (2019)
- [Unsupervised Domain Clusters in Pretrained Language Models](https://mlsanity.com/paper/2004.02105/) (2020)
- [LLMs4OL: Large Language Models for Ontology Learning](https://mlsanity.com/paper/2307.16648/) (2023)
- [Large Language Models Enable Few-Shot Clustering](https://mlsanity.com/paper/2307.00524/) (2023)
- [ClusterLLM: Large Language Models as a Guide for Text Clustering](https://mlsanity.com/paper/2305.14871/) (2023)
- [LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT](https://mlsanity.com/paper/2307.06917/) (2023)
- [OLaLa: Ontology Matching with Large Language Models](https://mlsanity.com/paper/2311.03837/) (2023)
- [Agent-OM: Leveraging LLM Agents for Ontology Matching](https://mlsanity.com/paper/2312.00326/) (2023)
- [Exploring Large Language Models for Ontology Alignment](https://mlsanity.com/paper/2309.07172/) (2023)
- [Towards Ontology Construction with Language Models](https://mlsanity.com/paper/2309.09898/) (2023)
- [Ontology engineering with Large Language Models](https://mlsanity.com/paper/2307.16699/) (2023)
- [An Experiment in Retrofitting Competency Questions for Existing Ontologies](https://mlsanity.com/paper/2311.05662/) (2023)
- [Knowledge Engineering using Large Language Models](https://mlsanity.com/paper/2310.00637/) (2023)
- [Using Large Language Models for Knowledge Engineering (LLMKE): A Case Study on Wikidata](https://mlsanity.com/paper/2309.08491/) (2023)
- [The Music Meta Ontology: a flexible semantic model for the interoperability of music metadata](https://mlsanity.com/paper/2311.03942/) (2023)

## Built on by (most cited first, 15 of 15)

- [LLMs4Life: Large Language Models for Ontology Learning in Life Sciences](https://mlsanity.com/paper/2412.02035/) (2024)
- [Large Language Models for Scholarly Ontology Generation: An Extensive Analysis in the Engineering Field](https://mlsanity.com/paper/2412.08258/) (2024)
- [Human Evaluation of Procedural Knowledge Graph Extraction from Text with Large Language Models](https://mlsanity.com/paper/2412.03589/) (2024)
- [Bench4KE: Benchmarking Automated Competency Question Generation](https://mlsanity.com/paper/2505.24554/) (2025)
- [Schema Generation for Large Knowledge Graphs Using Large Language Models](https://mlsanity.com/paper/2506.04512/) (2025)
- [A Comparative Study of Competency Question Elicitation Methods from Ontology Requirements](https://mlsanity.com/paper/2507.02989/) (2025)
- [The AnIML Ontology: Enabling Semantic Interoperability for Large-Scale Experimental Data in Interconnected Scientific Labs](https://mlsanity.com/paper/2604.01728/) (2026)
- [Large Language Models Assisting Ontology Evaluation](https://mlsanity.com/paper/2507.14552/) (2025)
- [Open Ontologies: Tool-Augmented Ontology Engineering with Stable Matching Alignment](https://mlsanity.com/paper/2605.09184/) (2026)
- [LLM-Enhanced Semantic Data Integration of Electronic Component Qualifications in the Aerospace Domain](https://mlsanity.com/paper/2603.20094/) (2026)
- [IDEA2: Expert-in-the-loop competency question elicitation for collaborative ontology engineering](https://mlsanity.com/paper/2604.01344/) (2026)
- [Characterising LLM-Generated Competency Questions: a Cross-Domain Empirical Study using Open and Closed Models](https://mlsanity.com/paper/2604.16258/) (2026)
- [BLINKG: A Benchmark for LLM-Integrated Knowledge Graph Generation](https://mlsanity.com/paper/2605.19518/) (2026)
- [Ontology-Mediated Neurosymbolic Constraint Acquisition from Multiple Stakeholders](https://mlsanity.com/paper/2609.29876/) (2026)
- [DTScribe: Generating Models for Digital Twin Reporting using Local Large Language Models](https://mlsanity.com/paper/2610.18635/) (2026)

## Abstract (arXiv)

Ontology engineering (OE) in large projects poses a number of challenges arising from the heterogeneous backgrounds of the various stakeholders, domain experts, and their complex interactions with ontology designers. This multi-party interaction often creates systematic ambiguities and biases from the elicitation of ontology requirements, which directly affect the design, evaluation and may jeopardise the target reuse. Meanwhile, current OE methodologies strongly rely on manual activities (e.g., interviews, discussion pages). After collecting evidence on the most crucial OE activities, we introduce OntoChat, a framework for conversational ontology engineering that supports requirement elicitation, analysis, and testing. By interacting with a conversational agent, users can steer the creation of user stories and the extraction of competency questions, while receiving computational support to analyse the overall requirements and test early versions of the resulting ontologies. We evaluate OntoChat by replicating the engineering of the Music Meta Ontology, and collecting preliminary metrics on the effectiveness of each component from users. We release all code at https://github.com/King-s-Knowledge-Graph-Lab/OntoChat.