An LLM-Driven Ensemble Framework for Constructing Legal Knowledge Graphs from Legislative Corpora
Xinchun Zhang, Neda Sakhaee, Iman Ardekani
Research Square
Problems Identified (4)
Unstructured legal text structuring: The paper addresses the challenge of converting unstructured legal text into a structured representation.
Legal graph analysis: The paper addresses the need to analyze legal information represented as a graph.
Unstructured legal text structuring: The paper addresses the challenge of converting unstructured legal text into a structured representation.
Legal graph analysis: The paper addresses the need to analyze legal information represented as a graph.
Proposed Solutions (5)
LLM ensemble legal KG construction: The paper proposes an LLM-driven ensemble framework for constructing legal knowledge graphs from legislative corpora.
Formal graph theory analysis: The framework analyzes a legislative knowledge graph using formal graph theory methods.
Legislative KG framework: The paper presents a scalable framework for building a knowledge graph from New Zealand legislative acts.
LLM ensemble legal KG construction: The paper proposes an LLM-driven ensemble framework for constructing legal knowledge graphs from legislative corpora.
Formal graph theory analysis: The framework analyzes a legislative knowledge graph using formal graph theory methods.
Research Domain
Artificial Intelligence in Law; legal knowledge graph construction