用大模型在维基数据框架下自动构建可解释的知识图谱
Ontology-grounded Automatic Knowledge Graph Construction by LLM under Wikidata schema
- 基于知识库生成能力问题，提取关系并映射到维基数据
- 在基准数据集上表现优异，结果与维基数据语义兼容
- 适合需要高质量、可读性强且可扩展知识库的研究者
我们提出一种基于本体的大型语言模型知识图谱构建方法。通过在知识库上生成能力问题（CQ）来发现知识范围，从CQ中提取关系，并尝试用维基数据中的等价关系替代原有关系。为确保最终知识图谱的一致性与可解释性，我们基于提取的关系构建本体，并以此约束知识图谱生成过程。在基准数据集上的评估表明，该方法在知识图谱构建任务中表现具有竞争力。本工作展示了无需人工干预即可实现规模化、高质量且人类可读知识图谱的可行方向，其结果可与维基数据语义互操作，具备潜在的知识库扩展能力。
原文摘要 · Abstract (English)
We propose an ontology-grounded approach to Knowledge Graph (KG) construction using Large Language Models (LLMs) on a knowledge base. An ontology is authored by generating Competency Questions (CQ) on knowledge base to discover knowledge scope, extracting relations from CQs, and attempt to replace equivalent relations by their counterpart in Wikidata. To ensure consistency and interpretability in the resulting KG, we ground generation of KG with the authored ontology based on extracted relations. Evaluation on benchmark datasets demonstrates competitive performance in knowledge graph construction task. Our work presents a promising direction for scalable KG construction pipeline with minimal human intervention, that yields high quality and human-interpretable KGs, which are interoperable with Wikidata semantics for potential knowledge base expansion.
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