Integrating knowledge graphs and contextual patterns for legal concept alignment

Area of Science:

• Artificial Intelligence
• Legal Informatics
• Computational Linguistics

Background:

• Growing need for complex legal analysis.
• Challenges in semantic understanding and interpretability of judicial decision-making.
• Difficulties in identifying relevant legal concepts from fact descriptions.

Purpose of the Study:

• To propose a novel framework for AI-assisted legal concept alignment.
• To address challenges in reasoning signals, statute relationships, and wording similarity.
• To improve the accuracy and interpretability of legal concept identification.

Main Methods:

• Integration of contextual pattern identification and law-centric knowledge graph construction.
• Utilizing hierarchical architectures for complex legal relationships.
• Employing a multi-perspective scoring mechanism for ranking quality.

Main Results:

• Significant improvement in legal concept alignment compared to baseline methods.
• Effective capture of fine-grained details and broad semantic relations in legal content.
• Demonstrated ability to identify partial relevance among multiple law articles.

Conclusions:

• The proposed framework enhances AI-assisted legal concept alignment.
• Prioritizes model interpretability and reasoning traceability alongside accuracy.
• Offers a valuable approach for complex legal analysis and judicial decision support.