本体引导的知识图谱构建：从结构化元数据与气候科学论文抽取知识（arXiv 论文）
Ontology-Guided Knowledge Graph Construction from Structured Metadata and Climate Science Publications
AI 导读
一项 arXiv 研究提出基于智能体、本体引导的知识图谱构建框架，由专用 Agent 负责文档理解、信息抽取、本体一致性与实体链接，LLM 抽取带文本证据的三元组并经多阶段验证。
正文 · 原文
Abstract:The rapid expansion of scientific literature far outpaces the speed of manual curation, creating a fundamental bottleneck in extracting structured knowledge from scientific texts. To address this, we introduce an agentic, ontology-guided framework for knowledge graph (KG) construction. Our framework utilizes specialized agents for document understanding, information extraction, ontology conformance, and entity linking. First, an LLM extracts factual knowledge in the form of triples with explicit textual evidence from research papers. Then the triplets undergo a multistage verification process to ground the triplets in the ontology. By enforcing schema constraints and requiring evidence in the form of text fragments, we ensure the data is both semantically consistent and traceable. In an agreement study over 1,000 extracted triplets, Gemini-2.5-flash achieves Cohen's $\kappa$ scores of 0.640--0.700 against three human annotators, matching the human--human agreement range of 0.616--0.689. We applied this framework to 2,000 scientific publications and construct a novel, ontology-grounded KG for climate science with 133,990 nodes and 755,308 relations; of the 23,327 triplets that the publications contribute after validation, 98.96\% are judged correct. The pipeline code and KG is publicly available this https URL.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.32725 [cs.CL] |
| (or arXiv:2610.32725v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.32725 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Aayush Acharya [view email]
[v1]
Fri, 23 Oct 2026 14:46:13 UTC (11,855 KB)
来源：arXiv cs.AI · arxiv.org