LINK-KG：基于LLM-Driven Correference Resolved知识图谱的人口走私网络
LINK-KG: LLM-Driven Coreference-Resolved Knowledge Graphs for Human Smuggling Networks
摘要：
人口偷运网络复杂且不断演变，因此难以对其进行全面分析。法律案例文档为这些网络提供了丰富的事实和程序见解，但通常很长、无结构，并且充满了模糊或不断变化的引用，这对自动化知识图谱（KG）构建构成了重大挑战。现有的方法要么忽略了共指消解，要么无法扩展到短文本范围之外，导致碎片化的图形和不一致的实体链接。我们提出LINK-KG，一个模块化的框架，集成了一个三阶段，LLM引导的共指消解管道与下游KG提取。我们的方法的核心是一个特定类型的提示缓存，它始终跟踪和解决跨文档块的引用，使清洁和消除歧义的叙述结构化的知识图谱建设从短期和长期的法律文本。与基线方法相比，LINK-KG减少了45.21%的平均节点重复和32.22%的噪声节点，从而使图形结构更清晰，更连贯。这些改进使LINK-KG成为分析复杂犯罪网络的坚实基础。
英文摘要：
Human smuggling networks are complex and constantly evolving, making them difficult to analyze comprehensively. Legal case documents offer rich factual and procedural insights into these networks but are often long, unstructured, and filled with ambiguous or shifting references, posing significant challenges for automated knowledge graph (KG) construction. Existing methods either overlook coreference resolution or fail to scale beyond short text spans, leading to fragmented graphs and inconsistent entity linking. We propose LINK-KG, a modular framework that integrates a three-stage, LLM-guided coreference resolution pipeline with downstream KG extraction. At the core of our approach is a type-specific Prompt Cache, which consistently tracks and resolves references across document chunks, enabling clean and disambiguated narratives for structured knowledge graph construction from both short and long legal texts. LINK-KG reduces average node duplication by 45.21% and noisy nodes by 32.22% compared to baseline methods, resulting in cleaner and more coherent graph structures. These improvements establish LINK-KG as a strong foundation for analyzing complex criminal networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
