ENTERPRISE AI ANALYSIS
Inside CORE-KG: Evaluating Structured Prompting and Coreference Resolution for Knowledge Graphs
This paper presents a systematic ablation study of CORE-KG, a framework for building clean and interpretable knowledge graphs from complex legal documents. It quantifies the individual contributions of type-aware coreference resolution and domain-guided structured prompts to reduce node duplication and legal noise, offering crucial insights for robust LLM-based KG pipelines in legal domains.
Executive Impact
The CORE-KG framework significantly enhances knowledge graph quality in legal contexts by optimizing entity resolution and noise reduction. These improvements lead to more accurate legal analysis and better decision-making for complex investigations.
Reduction in Node Duplication with Coreference
Reduction in Noisy Nodes with Structured Prompts
Average Noise Reduction
Average Entity Resolution Improvement
Deep Analysis & Enterprise Applications
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Impact of Coreference Resolution on KG Quality
Removing the coreference resolution module significantly increases node duplication by 28.32% and noisy nodes by 4.32%. This indicates its critical role in unifying disparate mentions and maintaining graph coherence.
Impact of Structured Prompting on KG Quality
The absence of structured prompts leads to a dramatic 73.33% increase in noisy nodes and a 4.34% increase in node duplication. Structured prompts are crucial for guiding the LLM to extract relevant entities and filter boilerplate.
CORE-KG Pipeline Overview
The CORE-KG framework integrates a type-aware coreference resolution module and domain-guided structured prompts. This modular design sequentially resolves contextually similar mentions and guides the LLM to extract relevant entities and relationships, significantly reducing node duplication and legal noise.
Qualitative Analysis of Extracted Graphs
A qualitative review of graphs shows that CORE-KG produces a more compact, coherent structure with minimal duplication and noise. The R/N ratio is highest in CORE-KG, demonstrating superior structural quality compared to ablation variants.
28.32%
Increase in Node Duplication without Coreference Resolution
73.33%
Increase in Noisy Nodes without Structured Prompts
Enterprise Process Flow
Legal Text Input
→
Type-Aware Coreference Resolution
→
Structured Prompting for Entity Extraction
→
Knowledge Graph Construction
| Method | Node Duplication (%) | Noisy Nodes (%) |
|---|---|---|
| GraphRAG | 30.53% (+50.61%) | 27.43% (+64.77%) |
| CoreKG-no-coref | 26.01% (+28.25%) | 17.37% (+4.32%) |
| CoreKG-no-structprompts | 21.15% (+4.34%) | 28.86% (+73.33%) |
| CORE-KG | 20.28% (-) | 16.65% (-) |
Case Study: Human Smuggling Networks
The CORE-KG framework was applied to U.S. federal and state court proceedings related to human smuggling networks. This complex domain involves unstructured legal texts with ambiguous references and legal boilerplate. CORE-KG's ability to reduce node duplication and noise significantly improves the clarity and actionability of extracted intelligence for law enforcement and policy makers, enhancing efforts to disrupt illicit operations.
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