mlsanityData to 14 Oct 2026

OntoChat: a Framework for Conversational Ontology Engineering using Language Models

arXiv 2403.05921 ↗First posted 9 Mar 2024cs.AIcited by 15

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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.

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.

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16 of 33 references.

References · 16

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2019Gradio: Hassle-Free Sharing and Testing of ML Models in the Wildcited by 178
  3. 2020Unsupervised Domain Clusters in Pretrained Language Modelscited by 160
  4. 2023LLMs4OL: Large Language Models for Ontology Learningcited by 77
  5. 2023Large Language Models Enable Few-Shot Clusteringcited by 72
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  7. 2023LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPTcited by 42
  8. 2023OLaLa: Ontology Matching with Large Language Modelscited by 35
  9. 2023Agent-OM: Leveraging LLM Agents for Ontology Matchingcited by 27
  10. 2023Exploring Large Language Models for Ontology Alignmentcited by 26
  11. 2023Towards Ontology Construction with Language Modelscited by 23
  12. 2023Ontology engineering with Large Language Modelscited by 17
4 more
  1. 2023An Experiment in Retrofitting Competency Questions for Existing Ontologiescited by 12
  2. 2023Knowledge Engineering using Large Language Modelscited by 9
  3. 2023Using Large Language Models for Knowledge Engineering (LLMKE): A Case Study on Wikidatacited by 5
  4. 2023The Music Meta Ontology: a flexible semantic model for the interoperability of music metadatacited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 4; 2026: 8.

15 papers.

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  1. 2024LLMs4Life: Large Language Models for Ontology Learning in Life Sciencescited by 12
  2. 2024Large Language Models for Scholarly Ontology Generation: An Extensive Analysis in the Engineering Fieldcited by 10
  3. 2024Human Evaluation of Procedural Knowledge Graph Extraction from Text with Large Language Modelscited by 4
  4. 2025Bench4KE: Benchmarking Automated Competency Question Generationcited by 3
  5. 2025Schema Generation for Large Knowledge Graphs Using Large Language Modelscited by 3
  6. 2025A Comparative Study of Competency Question Elicitation Methods from Ontology Requirementscited by 3
  7. 2026The AnIML Ontology: Enabling Semantic Interoperability for Large-Scale Experimental Data in Interconnected Scientific Labscited by 3
  8. 2025Large Language Models Assisting Ontology Evaluationcited by 2
  9. 2026Open Ontologies: Tool-Augmented Ontology Engineering with Stable Matching Alignmentcited by 2
  10. 2026LLM-Enhanced Semantic Data Integration of Electronic Component Qualifications in the Aerospace Domaincited by 0
  11. 2026IDEA2: Expert-in-the-loop competency question elicitation for collaborative ontology engineeringcited by 0
  12. 2026Characterising LLM-Generated Competency Questions: a Cross-Domain Empirical Study using Open and Closed Modelscited by 0
3 more
  1. 2026BLINKG: A Benchmark for LLM-Integrated Knowledge Graph Generationcited by 0
  2. 2026Ontology-Mediated Neurosymbolic Constraint Acquisition from Multiple Stakeholderscited by 0
  3. 2026DTScribe: Generating Models for Digital Twin Reporting using Local Large Language Modelscited by 0