Technology Category
Philosophy and Ethics of AI: AI & Law, Justice, Regulation & GovernanceKnowledge Representation and Reasoning: OntologiesData Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
Computational Law: Datasets, Benchmarks, and Ontologies
🤖 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
Application Category
📝 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
Research questions and friction points this paper is trying to address.
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
Methods, ideas, or system contributions that make the work stand out.
Machine learning models for legal data analysis
Ontologies for legal system interoperability
Datasets and benchmarks for computational law
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