论文研究
arxiv.org ·
GLaMoR：使用图语言模型进行 OWL 本体一致性检查
该论文提出 GLaMoR，将 OWL 本体转换为图结构数据并改造 Graph Language Model 架构，用于本体一致性检查。作者在 NCBO BioPortal 仓库的本体上评测，称 GLM 优于所有基线模型，达到 95% 准确率，并比经典 reasoner 快 20 倍。研究指出经典 reasoner 计算开销大且随本体规模增长效率下降，而此前机器学习方法多针对 A-Box 公理，T-Box 尚未被处理。
AI推荐理由 arXiv 论文（v2，2026-09-11 修订），来源可信、摘要信息密度高。提出 GLaMoR，将 OWL 本体转为图结构并改造 Graph Language Model 做一致性检查，在 NCBO BioPortal 本体上报告 95% 准确率、比经典 reasoner 快 20 倍。新意在于此前一致性检查多针对 A-Box，本文覆盖 T-Box；局限是仅在 BioPortal 本体上评测，摘要未给出与具体基线对比细节，95% 与 20 倍为作者自报结果，尚待复现。
72
正文
Computer Science > Artificial Intelligence
arXiv:2504.19023 (cs)
[Submitted on 26 Apr 2025 (v1), last revised 11 Sep 2026 (this version, v2)]
Title:GLaMoR: Consistency Checking of OWL Ontologies using Graph Language Models
Authors:Justin Mücke, Ansgar Scherp
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Abstract:Semantic reasoning aims to infer new knowledge from existing knowledge, with OWL ontologies serving as a standardized framework for organizing information. A key challenge in semantic reasoning is verifying ontology consistency. However, state-of-the-art reasoners are computationally expensive, and their efficiency decreases as ontology sizes grow. While classical machine learning models have been explored for consistency checking of A-Box axioms, considering T-Boxes remains unaddressed. Large language models (LLMs) have shown promising results for natural language inference but perform poorly on logical reasoning. The recently introduced Graph Language Model (GLM) offers a way to simultaneously process graph-structured data and text. This paper proposes GLaMoR (Graph Language Model for Reasoning), a reasoning pipeline that transforms OWL ontologies into graph-structured data and adapts the GLM architecture for consistency checking. We evaluate GLaMoR on ontologies from the NCBO BioPortal repository, converting them into triples suitable for model input. Our results show that the GLM outperforms all baseline models, achieving $95\%$ accuracy, and is $20$ times faster than classical reasoners.
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Subjects:
Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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arXiv:2504.19023 [cs.AI]
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arXiv:2504.19023v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2504.19023
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arXiv-issued DOI via DataCite
Submission history
From: Ansgar Scherp [view email]
[v1]
Sat, 26 Apr 2025 21:20:29 UTC (186 KB)
[v2]
Fri, 11 Sep 2026 11:02:02 UTC (170 KB)
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