Explainable Reasoning with Legal Big Data: A Layered Framework

Grigoris Antoniou, Katie Atkinson, George Baryannis, Sotiris Batsakis, Luigi Di Caro, Guido Governatori, Livio Robaldo, Giovanni Siragusa, Ilias Tachmazidis

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Knowledge representation and reasoning in the legal domain has primarily focused on case studies where knowledge and data can fit in main memory. However, this assumption no longer applies in the era of big data, where large amounts of data are generated daily. This paper discusses new opportunities and challenges that emerge in relation to reasoning with legal big data and the concepts of volume, velocity, variety and veracity. A four-layer legal big data framework is proposed to manage the complete lifecycle of legal big data from sourcing, processing and storage, to reasoning, analysis and consumption. Within each layer, a number of relevant future research directions are also identified, which can facilitate the realisation of knowledge-rich legal big data solutions.

Original languageEnglish
Pages (from-to)1155-1170
Number of pages16
JournalIfCoLoG Journal of Logics and their Applications
Issue number4
Publication statusPublished - 1 Jul 2022


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