CORE-KG: An LLM-Driven Knowledge Graph Construction Framework for Human Smuggling Networks
This addresses the challenge of analyzing adaptive human smuggling networks for legal or investigative purposes, though it is incremental as it builds on existing GraphRAG methods.
The paper tackled the problem of constructing knowledge graphs from unstructured legal case documents on human smuggling networks, proposing CORE-KG, which reduced node duplication by 33.28% and legal noise by 38.37% compared to a baseline.
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.