用图语言模型加速知识库一致性检查，快20倍还更准。
GLaMoR: Consistency Checking of OWL Ontologies using Graph Language Models
- 将OWL本体转为图结构，用图语言模型进行推理
- 在生物本体库上达95%准确率，比传统方法快20倍
- 适合需要快速验证大规模知识库的开发者
语义推理旨在从已有知识中推断新知识，而OWL本体是组织信息的标准框架。其核心挑战在于验证本体的一致性。然而，现有推理机计算成本高，且随着本体规模增大效率下降。尽管已有经典机器学习模型尝试用于一致性检查，但难以捕捉本体中的复杂关系。大语言模型虽在简单推理任务中表现良好，但在结构化推理上表现不佳。最近提出的图语言模型（GLM）可同时处理图结构数据和文本。本文提出GLaMoR（Graph Language Model for Reasoning），将OWL本体转换为图结构数据，并适配GLM架构用于一致性检查。我们在NCBO BioPortal仓库的本体上进行评估，将其转换为模型输入所需的三元组形式。结果表明，该方法优于所有基线模型，在测试集上达到95%准确率，同时比传统推理机快20倍。
原文摘要 · Abstract (English)
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, they struggle to capture complex relationships within ontologies. Large language models (LLMs) have shown promising results for simple reasoning tasks but perform poorly on structured 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 while being 20 times faster than classical reasoners. The Code is accessible under: https://github.com/JustinMuecke/GLaMoR
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