Explainable Reasoning with Legal Big Data: A Layered Framework

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Antoniou, Grigoris
Atkinson, Katie
Baryannis, George
Batsakis, Sotiris
Di Caro, Luigi
Governatori, Guido
Robaldo, Livid
Siragusa, Giovanni
Tachmazidis, Ilias
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2022
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http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract

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.

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Journal of Applied Logics — IfCoLog Journal of Logics and their Applications
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9
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4
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© The Individual authors and College Publications 2022. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International (CC BY-NC-ND 4.0) License, which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
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Science & Technology
Logic
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Antoniou, G; Atkinson, K; Baryannis, G; Batsakis, S; Di Caro, L; Governatori, G; Robaldo, L; Siragusa, G; Tachmazidis, I, Explainable Reasoning with Legal Big Data: A Layered Framework, Journal of Applied Logics — IfCoLog Journal of Logics and their Applications, 2022, 9 (4), pp. 1111-
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