mlsanityData to 7 Oct 2026

LLMs4OM: Matching Ontologies with Large Language Models

arXiv 2404.10317 ↗First posted 16 Apr 2024cs.AIcited by 22

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Abstract (arXiv)

Ontology Matching (OM), is a critical task in knowledge integration, where aligning heterogeneous ontologies facilitates data interoperability and knowledge sharing. Traditional OM systems often rely on expert knowledge or predictive models, with limited exploration of the potential of Large Language Models (LLMs). We present the LLMs4OM framework, a novel approach to evaluate the effectiveness of LLMs in OM tasks. This framework utilizes two modules for retrieval and matching, respectively, enhanced by zero-shot prompting across three ontology representations: concept, concept-parent, and concept-children. Through comprehensive evaluations using 20 OM datasets from various domains, we demonstrate that LLMs, under the LLMs4OM framework, can match and even surpass the performance of traditional OM systems, particularly in complex matching scenarios. Our results highlight the potential of LLMs to significantly contribute to the field of OM.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 22 of its 50 references that are in the map. First posted on arXiv 16 Apr 2024.

Builds on

22 of 50 references.

References · 22

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Citing papers per year: 2024: 4; 2025: 11; 2026: 7.

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  1. 2025Large language models for artificial general intelligence (AGI): A survey of foundational principles and approachescited by 18
  2. 2024Matchmaker: Self-Improving Large Language Model Programs for Schema Matchingcited by 9
  3. 2024LLMs4OL 2024 Overview: The 1st Large Language Models for Ontology Learning Challengecited by 8
  4. 2025OntoAligner: A Comprehensive Modular and Robust Python Toolkit for Ontology Alignmentcited by 8
  5. 2025Ontology Matching with Large Language Models and Prioritized Depth-First Searchcited by 5
  6. 2025Large Language Models as Oracles for Ontology Alignmentcited by 3
  7. 2025Addressing Longstanding Challenges in Cognitive Science with Language Modelscited by 3
  8. 2025KROMA: Ontology Matching with Knowledge Retrieval and Large Language Modelscited by 2
  9. 2025GenOM: Ontology Matching with Description Generation and Large Language Modelcited by 2
  10. 2026A Guide to Large Language Models in Modeling and Simulation: From Core Techniques to Critical Challengescited by 2
  11. 2024DSTI at LLMs4OL 2024 Task A: Intrinsic versus extrinsic knowledge for type classificationcited by 1
  12. 2025Reviewing Clinical Knowledge in Medical Large Language Models: Training and Beyondcited by 1
10 more
  1. 2025OAEI-LLM-T: A TBox Benchmark Dataset for Understanding Large Language Model Hallucinations in Ontology Matchingcited by 1
  2. 2025OntoAligner Meets Knowledge Graph Embedding Alignerscited by 1
  3. 2024How Does A Text Preprocessing Pipeline Affect Ontology Matching?cited by 0
  4. 2025Generating Natural-Language Surgical Feedback: From Structured Representation to Domain-Grounded Evaluationcited by 0
  5. 2026Ontological Trajectory Forecasting via Finite Semigroup Iteration and Lie Algebra Approximation in Geopolitical Knowledge Graphscited by 0
  6. 2026AgentMap: Joint Equivalence and Subsumption Discovery for Ontology Matchingcited by 0
  7. 2026Consistency Is Not Coherence: Orientation Search for Certified Alignments Between 4D Defence Upper Ontologiescited by 0
  8. 2026OntoAligner-Ensemble: Voting-Based Fusion across Heterogeneous Ontology Alignment Techniquescited by 0
  9. 2026CRASM-Gate: Deterministic-First Constraint- and Role-Aware Semantic Mapping with Selective Model Assistance Across Heterogeneous Industrial Standardscited by 0
  10. 2026Federated Agent Optimizationcited by 0