用大模型构建更清晰的人口走私网络知识图谱
CORE-KG: An LLM-Driven Knowledge Graph Construction Framework for Human Smuggling Networks
- 分两步走：先解决指代歧义，再精准提取实体关系
- 节点重复减少33.28%，法律文本噪声降低38.37%
- 适合司法分析、犯罪网络研究者使用
人口走私网络日益复杂且具适应性，法律案件文档虽含关键信息，但内容非结构化、词汇密集且指代模糊，给自动化知识图谱（KG）构建带来挑战。现有方法多依赖静态模板，缺乏指代消解能力；近期基于大模型的方法常因幻觉产生噪声和碎片化图谱，且因缺乏引导导致节点重复。我们提出CORE-KG，一个模块化框架，用于从法律文本中构建可解释的知识图谱。该框架采用两阶段流程：(1) 通过序列化、结构化的提示，实现类型感知的指代消解；(2) 基于领域引导指令，在改进的GraphRAG框架上进行实体与关系抽取。相比基于GraphRAG的基线，CORE-KG将节点重复率降低33.28%，法律文本噪声减少38.37%，生成的图谱更清晰、连贯。该成果为复杂犯罪网络分析提供了可靠基础。
原文摘要 · Abstract (English)
Human smuggling networks are increasingly adaptive and difficult to analyze. Legal case documents offer valuable insights but are unstructured, lexically dense, and filled with ambiguous or shifting references-posing challenges for automated knowledge graph (KG) construction. Existing KG methods often rely on static templates and lack coreference resolution, while recent LLM-based approaches frequently produce noisy, fragmented graphs due to hallucinations, and duplicate nodes caused by a lack of guided extraction. We propose CORE-KG, a modular framework for building interpretable KGs from legal texts. It uses a two-step pipeline: (1) type-aware coreference resolution via sequential, structured LLM prompts, and (2) entity and relationship extraction using domain-guided instructions, built on an adapted GraphRAG framework. CORE-KG reduces node duplication by 33.28%, and legal noise by 38.37% compared to a GraphRAG-based baseline-resulting in cleaner and more coherent graph structures. These improvements make CORE-KG a strong foundation for analyzing complex criminal networks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。