用大模型自动构建企业知识图谱本体，提升效率。
LLM-Driven Ontology Construction for Enterprise Knowledge Graphs
- 分两阶段：先提取核心类与属性，再逻辑建模成层级结构。
- 在数据领域达0.724的模糊匹配F1分数，但层次推理仍有不足。
- 适合需要快速构建行业本体的企业和研究者。
企业知识图谱已成为统一异构数据和实施语义治理的关键工具。然而，其底层本体的构建仍依赖大量人力和领域专家知识，过程繁琐。本文提出OntoEKG，一种基于大语言模型的流水线方法，旨在从非结构化企业数据中加速生成特定领域的本体。该方法将建模任务分解为两个阶段：提取模块识别核心类与属性，推理模块则对这些元素进行逻辑结构化，形成层级关系后序列化为标准RDF格式。针对现有端到端本体构建缺乏全面评估基准的问题，我们采用来自数据、金融和物流领域的文档构建新评测数据集。实验结果表明，该方法在数据领域取得0.724的模糊匹配F1分数，展现出巨大潜力，同时也暴露出范畴定义不清和层级推理能力有限等挑战。
原文摘要 · Abstract (English)
Enterprise Knowledge Graphs have become essential for unifying heterogeneous data and enforcing semantic governance. However, the construction of their underlying ontologies remains a resource-intensive, manual process that relies heavily on domain expertise. This paper introduces OntoEKG, a LLM-driven pipeline designed to accelerate the generation of domain-specific ontologies from unstructured enterprise data. Our approach decomposes the modelling task into two distinct phases: an extraction module that identifies core classes and properties, and an entailment module that logically structures these elements into a hierarchy before serialising them into standard RDF. Addressing the significant lack of comprehensive benchmarks for end-to-end ontology construction, we adopt a new evaluation dataset derived from documents across the Data, Finance, and Logistics sectors. Experimental results highlight both the potential and the challenges of this approach, achieving a fuzzy-match F1-score of 0.724 in the Data domain while revealing limitations in scope definition and hierarchical reasoning.
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