Integrating knowledge graphs and contextual patterns for legal concept alignment

AI-assisted legal concept alignment, defined as identifying legal concepts that best correspond to a given fact description based on computational relevance, addresses the growing need for complex legal analysis that requires both semantic understanding and interpretability of judicial decision-making. This task presents three main challenges: reasoning-related contextual signals in judicial texts, complex hierarchical and cross-referential relationships among statutes, and highly similar wording in legal content. To address these challenges, this paper proposes a framework that integrates contextual pattern identification and law-centric knowledge graph construction, supported by hierarchical architectures and a multi-perspective scoring mechanism. Contextual pattern identification captures reasoning-related contextual signals within judicial texts. Law-centric knowledge graph and hierarchical architectures collectively address the problems of complex cross-referential relationships among legal elements and semantic ambiguity posed by closely related legal content. A multi-perspective scoring mechanism further evaluates the ranking quality of selected law articles. Experimental results show that the proposed framework captures both fine-grained details and broad semantic relations in legal content, achieving significant improvement in legal concept alignment compared with baseline methods. This paper delivers distinct value in pursuing ranking quality over pure alignment accuracy, capturing partial relevance among multiple law articles, and prioritizing model interpretability and reasoning traceability.