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

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 journal › Article › peer-review

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.

Original language	English
Pages (from-to)	1155-1170
Number of pages	16
Journal	IfCoLoG Journal of Logics and their Applications
Volume	9
Issue number	4
Publication status	Published - 1 Jul 2022

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Cite this

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AU - Caro, Luigi Di

AU - Governatori, Guido

AU - Robaldo, Livio

AU - Siragusa, Giovanni

AU - Tachmazidis, Ilias

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Explainable Reasoning with Legal Big Data: A Layered Framework

Abstract

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