Computational Law: Datasets, Benchmarks, and Ontologies
This is an incremental review paper that addresses the need for domain-specific data and semantic resources to support high-performance computational law systems.
The paper provides an up-to-date review of datasets, benchmarks, and ontologies for computational law, aiming to assist researchers and practitioners in developing and testing systems in this domain.
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.