KROMA: Ontology Matching with Knowledge Retrieval and Large Language Models

TL;DR

KROMA introduces a novel ontology matching framework that leverages large language models with knowledge retrieval, improving accuracy and efficiency over existing methods through context enrichment and ontology refinement.

Contribution

The paper presents KROMA, a new OM approach combining LLMs with retrieval-augmented generation and optimization techniques for scalable, accurate ontology matching.

Findings

Outperforms classic and LLM-based OM systems on benchmark datasets.

Significantly reduces communication overhead with ontology refinement.

Enhances semantic context using knowledge retrieval and prompt enrichment.

Abstract

Ontology Matching (OM) is a cornerstone task of semantic interoperability, yet existing systems often rely on handcrafted rules or specialized models with limited adaptability. We present KROMA, a novel OM framework that harnesses Large Language Models (LLMs) within a Retrieval-Augmented Generation (RAG) pipeline to dynamically enrich the semantic context of OM tasks with structural, lexical, and definitional knowledge. To optimize both performance and efficiency, KROMA integrates a bisimilarity-based concept matching and a lightweight ontology refinement step, which prune candidate concepts and substantially reduce the communication overhead from invoking LLMs. Through experiments on multiple benchmark datasets, we show that integrating knowledge retrieval with context-augmented LLMs significantly enhances ontology matching, outperforming both classic OM systems and cutting-edge LLM-based approaches while keeping communication overhead comparable. Our study highlights the feasibility and benefit of the proposed optimization techniques (targeted knowledge retrieval, prompt enrichment, and ontology refinement) for ontology matching at scale.