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Agent-OM: Leveraging LLM Agents for Ontology Matching
April 4, 2024, 4:48 a.m. | Zhangcheng Qiang, Weiqing Wang, Kerry Taylor
cs.CL updates on arXiv.org arxiv.org
Abstract: Ontology matching (OM) enables semantic interoperability between different ontologies and resolves their conceptual heterogeneity by aligning related entities. OM systems currently have two prevailing design paradigms: conventional knowledge-based expert systems and newer machine learning-based predictive systems. While large language models (LLMs) and LLM agents have revolutionised data engineering and have been applied creatively in many domains, their potential for OM remains underexplored. This study introduces a novel agent-powered LLM-based design paradigm for OM systems. With …
abstract agent agents arxiv cs.ai cs.cl cs.ir data data engineering design engineering expert interoperability knowledge language language models large language large language models llm llms machine machine learning ontologies ontology predictive semantic systems type
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