Computational Law: Datasets, Benchmarks, and Ontologies

There is a surge observed in research and applications of computer science and artificial intelligence in the legal domain. The related term computational law is commonly defined as "the branch of Legal Informatics concerned with the representation of rule and regulations in computable form" [Genesereth and Chaudhri, 2022]. The focus of an important percentage of related work on computational law is on automatic processing, generation, or understanding of legal documents [Küçük and Can, 2024]. Recent advancements in artificial intelligence (AI), such as generative AI models, pre-trained language models (PLMs) or large language models (LLMs), and chatbots developed using such models, have also affected the domain of computational law, and this dramatic impact is also acknowledged by legal professionals [Goth, 2024]. Undoubtedly, annotated or unannotated datasets and benchmarks in digital form are required for legal AI studies on legal texts, in order to facilitate model training, and to ensure sound comparisons of different approaches to the problems pertaining to computational law.