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Ontology engineering with Large Language Models

arXiv 2307.16699 ↗First posted 31 Jul 2023cs.AI cs.CLcited by 17

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Abstract (arXiv)

We tackle the task of enriching ontologies by automatically translating natural language sentences into Description Logic. Since Large Language Models (LLMs) are the best tools for translations, we fine-tuned a GPT-3 model to convert Natural Language sentences into OWL Functional Syntax. We employ objective and concise examples to fine-tune the model regarding: instances, class subsumption, domain and range of relations, object properties relationships, disjoint classes, complements, cardinality restrictions. The resulted axioms are used to enrich an ontology, in a human supervised manner. The developed tool is publicly provided as a Protge plugin.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 2 of its 16 references that are in the map. First posted on arXiv 31 Jul 2023.

Builds on

2 of 16 references.

References · 2

  1. 2023Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translationcited by 59
  2. 2023Structured prompt interrogation and recursive extraction of semantics (SPIRES): A method for populating knowledge bases using zero-shot learningcited by 50

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 7; 2025: 6; 2026: 2.

17 papers.

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  1. 2023OLaLa: Ontology Matching with Large Language Modelscited by 35
  2. 2025Ontology Generation using Large Language Modelscited by 23
  3. 2025LLM-empowered knowledge graph construction: A surveycited by 20
  4. 2024OntoChat: a Framework for Conversational Ontology Engineering using Language Modelscited by 14
  5. 2024LLMs4Life: Large Language Models for Ontology Learning in Life Sciencescited by 12
  6. 2024Do LLMs Really Adapt to Domains? An Ontology Learning Perspectivecited by 10
  7. 2025Retrieval-Augmented Generation of Ontologies from Relational Databasescited by 4
  8. 2025Ontology Learning with LLMs: A Benchmark Study on Axiom Identificationcited by 4
  9. 2024Towards Ontology-Enhanced Representation Learning for Large Language Modelscited by 3
  10. 2024Prompt-Time Ontology-Driven Symbolic Knowledge Capture with Large Language Modelscited by 2
  11. 2026OntoExtend: A Framework for Requirement-driven and Scalable Ontology Extension with LLMscited by 2
  12. 2025Knowledge Graph-Driven Retrieval-Augmented Generation: Integrating Deepseek-R1 with Weaviate for Advanced Chatbot Applicationscited by 1
5 more
  1. 2023Forest Mixing: investigating the impact of multiple search trees and a shared refinements pool on ontology learningcited by 0
  2. 2024Evaluating Class Membership Relations in Knowledge Graphs using Large Language Modelscited by 0
  3. 2024The Mercurial Top-Level Ontology of Large Language Modelscited by 0
  4. 2025Leveraging LLMs for Collaborative Ontology Engineering in Parkinson Disease Monitoring and Alertingcited by 0
  5. 2026JD Oxygen AI Item Center (Oxygen AIIC) V1: An Industrial-Scale LLM/VLM-Centric Solution for Item Understanding, Management, and Applicationscited by 0