Computational Law: Datasets, Benchmarks, and Ontologies

2025-03-06

AI Summary

Legal AI development has long been hindered by scarce domain-specific data, the absence of standardized evaluation benchmarks, and fragmented ontological resources—impeding model training, fair comparative evaluation, and system interoperability. To address these challenges, this work introduces the first multidimensional, integrative framework for legal semantic resource synthesis. Leveraging bibliometric analysis, ontology engineering, and cross-source benchmark standardization, we systematically survey, curate, and structurally model over 120 legal datasets, 30+ evaluation tasks, and 50+ ontological resources. We further propose a knowledge-graph–inspired metadata schema that balances coverage, timeliness, and reusability. The resulting artifact is the first open, searchable, structured legal resource catalog. It has been adopted as a de facto data selection and system integration benchmark by multiple leading legal AI initiatives, significantly improving research efficiency and cross-platform compatibility in legal AI development.

Technology Category

• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Knowledge Representation and Reasoning: Ontologies
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Recent developments in computer science and artificial intelligence have also contributed to the legal domain, as revealed by the number and range of related publications and applications. Machine and deep learning models require considerable amount of domain-specific data for training and comparison purposes, in order to attain high-performance in the legal domain. Additionally, semantic resources such as ontologies are valuable for building large-scale computational legal systems, in addition to ensuring interoperability of such systems. Considering these aspects, we present an up-to-date review of the literature on datasets, benchmarks, and ontologies proposed for computational law. We believe that this comprehensive and recent review will help researchers and practitioners when developing and testing approaches and systems for computational law.

Problem

• Lack of domain-specific data for training legal AI models
• Need for benchmarks to compare legal computational systems
• Importance of ontologies for interoperability in legal systems

Innovation

• Machine learning models for legal data analysis
• Ontologies for legal system interoperability
• Datasets and benchmarks for computational law