用大模型直接构建去重的百万级知识库，解决实体混淆问题。
GPTKB 2.0: Direct Construction of Disambiguated Knowledge Bases from Large Language Models
- 边生成边消歧实体、关系和类别，实现精准去重。
- 构建超100万实体、3840万三元组的高质量知识库。
- 适合需要高精度知识库的研究者与开发者使用。
自动化知识库构建（AKBC）是自然语言处理的核心任务。近期工作尝试直接从大语言模型（LLMs）生成知识库，将模型本身作为知识源。然而，LLMs原生缺乏实体表示，导致重复条目和混淆。我们提出GPTKB 2.0，一种直接从大模型构建去歧化知识库的方法。该方法集成实时实体、关系和类别的消歧机制，精心设计以兼顾可扩展性与去歧准确性。我们分析了核心设计决策，刻画了准确率、规模与成本之间的权衡。在大规模执行中，获得了包含超过100万去歧化实体和3840万三元组的可落地知识库。这是首个百万级、具备显式内部规范化（实体、关系、类别）的大模型原生知识库，显著区别于以往依赖维基百科的工作。GPTKB 2.0已开放访问：https://gptkb.org/。
原文摘要 · Abstract (English)
Automated Knowledge Base Construction (AKBC) is a core NLP task, and recent work proposes generating knowledge bases directly from large language models (LLMs), treating the model itself as the knowledge source. However, LLMs natively possess no representation of entities, leading to duplicate entries as well as conflations. We propose GPTKB 2.0, a methodology for constructing disambiguated KBs directly from LLMs. GPTKB 2.0 incorporates on-the-fly disambiguation of entities, relations and classes, and is meticulously designed to satisfy both scalability and disambiguation accuracy. We analyze the central design decisions and characterize the trade-offs between accuracy, scale, and cost. We execute GPTKB 2.0 at scale, obtaining a materialized KB containing over 1M disambiguated entities and 38.4M triples. This represents the first million-scale LLM-native KB with explicit internal canonicalization of entities, relations, and classes, a significant departure from prior Wikimedia-centric works. GPTKB 2.0 is available at https://gptkb.org/.
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