CORE-KG: An LLM-Driven Knowledge Graph Construction Framework for Human Smuggling Networks

• URL: http://arxiv.org/abs/2506.21607v1
• Date: Fri, 20 Jun 2025 11:58:00 GMT
• Title: CORE-KG: An LLM-Driven Knowledge Graph Construction Framework for Human Smuggling Networks
• Authors: Dipak Meher, Carlotta Domeniconi, Guadalupe Correa-Cabrera,
• Abstract summary: CORE-KG is a modular framework for building interpretable knowledge graphs from legal texts.<n>It reduces node duplication by 33.28%, and legal noise by 38.37% compared to a GraphRAG-based baseline.
• Score: 9.68109098750283
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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