LLM-Driven Knowledge Graph Construction for Human Smuggling Networks

Dipak Meher, Carlotta Domeniconi, Guadalupe Correa-Cabrera

Keywords: Knowledge Graph Construction, Coreference Resolution, Human Smuggling Networks

TL;DR: We propose CORE-KG, a prompt-guided framework that combines type-aware coreference resolution and structured LLM prompting to construct coherent, noise-reduced knowledge graphs from legal texts on human smuggling.

Abstract: 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.

Submission Number: 24