Legal Reasoning and Big Data: Opportunities and Challenges

Grigoris Antoniou; George Baryannis; Sotirios Batsakis; Guido Governatori; Livio Robaldo; Giovanni Siragusa; Ilias Tachmazidis

doi:10.29007/tkmv

Legal Reasoning and Big Data: Opportunities and Challenges

Grigoris Antoniou, George Baryannis, Sotirios Batsakis, Guido Governatori, Livio Robaldo, Giovanni Siragusa, Ilias Tachmazidis

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The main underlying assumption of traditional legal knowledge representation and reasoning is that knowledge and data are both available in main memory. However, in the era of big data, where large amounts of data are generated daily, an increasing range of scientific disciplines, as well as business and human activities, are becoming datadriven. This paper discusses new opportunities and potential applications of legal reasoning involving big data as well as the technical challenges associated with the main concepts of the big data landscape, namely volume, velocity, variety and veracity. Future research directions based on the identified challenges are also proposed.

Original language	English
Title of host publication	Proceedings of the MIREL 2018 workshop on MIning and REasoning with Legal texts
Publisher	EasyChair
Pages	1-15
Number of pages	15
DOIs	https://doi.org/10.29007/tkmv
Publication status	Published - 30 Aug 2018
Event	Mining and REasoning with Legal Texts - Luxembourg City, Luxembourg Duration: 17 Sep 2018 → 17 Sep 2018 https://sites.google.com/view/mirelworkshop2018/welcome (Link to Workshop Website)

Workshop

Workshop	Mining and REasoning with Legal Texts
Abbreviated title	MIREL
Country	Luxembourg
City	Luxembourg City
Period	17/09/18 → 17/09/18
Internet address	https://sites.google.com/view/mirelworkshop2018/welcome (Link to Workshop Website)

Access to Document

10.29007/tkmvLicence: Other

MIREL_2018_paper_1Accepted author manuscript, 197 KB

Link to publisher's website

1 Finished

MIREL: MIning and REasoning with Legal texts
Antoniou, G., Vallati, M., Tachmazidis, I., Mantle, M. & Bargiannis, G.
1/01/16 → 31/12/19
Project: Research

1 Article

Large-Scale Legal Reasoning with Rules and Databases
Antoniou, G., Baryannis, G., Batsakis, S., Tachmazidis, I., Governatori, G., Islam, M. B., Liu, Q., Robaldo, L. & Siragusa, G., 1 May 2021, In: IfCoLoG Journal of Logics and their Applications. 8, 4, p. 911-939 29 p.
Research output: Contribution to journal › Article › peer-review

Open Access

Cite this

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title = "Legal Reasoning and Big Data: Opportunities and Challenges",

abstract = "The main underlying assumption of traditional legal knowledge representation and reasoning is that knowledge and data are both available in main memory. However, in the era of big data, where large amounts of data are generated daily, an increasing range of scientific disciplines, as well as business and human activities, are becoming datadriven. This paper discusses new opportunities and potential applications of legal reasoning involving big data as well as the technical challenges associated with the main concepts of the big data landscape, namely volume, velocity, variety and veracity. Future research directions based on the identified challenges are also proposed.",

keywords = "Legal Reasoning, Big Data, Large-Scale Reasoning",

author = "Grigoris Antoniou and George Baryannis and Sotirios Batsakis and Guido Governatori and Livio Robaldo and Giovanni Siragusa and Ilias Tachmazidis",

year = "2018",

month = aug,

day = "30",

doi = "10.29007/tkmv",

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pages = "1--15",

booktitle = "Proceedings of the MIREL 2018 workshop on MIning and REasoning with Legal texts",

publisher = "EasyChair",

address = "United Kingdom",

note = "Mining and REasoning with Legal Texts, MIREL ; Conference date: 17-09-2018 Through 17-09-2018",

url = "https://sites.google.com/view/mirelworkshop2018/welcome",

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Antoniou, G , Baryannis, G , Batsakis, S, Governatori, G, Robaldo, L, Siragusa, G & Tachmazidis, I 2018, Legal Reasoning and Big Data: Opportunities and Challenges. in Proceedings of the MIREL 2018 workshop on MIning and REasoning with Legal texts., 470, EasyChair, pp. 1-15, Mining and REasoning with Legal Texts, Luxembourg City, Luxembourg, 17/09/18. https://doi.org/10.29007/tkmv

Legal Reasoning and Big Data : Opportunities and Challenges. / Antoniou, Grigoris ; Baryannis, George ; Batsakis, Sotirios; Governatori, Guido; Robaldo, Livio; Siragusa, Giovanni; Tachmazidis, Ilias.

Proceedings of the MIREL 2018 workshop on MIning and REasoning with Legal texts. EasyChair, 2018. p. 1-15 470.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Antoniou, Grigoris

AU - Baryannis, George

AU - Batsakis, Sotirios

AU - Governatori, Guido

AU - Robaldo, Livio

AU - Siragusa, Giovanni

AU - Tachmazidis, Ilias

PY - 2018/8/30

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AB - The main underlying assumption of traditional legal knowledge representation and reasoning is that knowledge and data are both available in main memory. However, in the era of big data, where large amounts of data are generated daily, an increasing range of scientific disciplines, as well as business and human activities, are becoming datadriven. This paper discusses new opportunities and potential applications of legal reasoning involving big data as well as the technical challenges associated with the main concepts of the big data landscape, namely volume, velocity, variety and veracity. Future research directions based on the identified challenges are also proposed.

KW - Legal Reasoning

KW - Big Data

KW - Large-Scale Reasoning

UR - http://www.mirelproject.eu/publications.php

U2 - 10.29007/tkmv

DO - 10.29007/tkmv

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BT - Proceedings of the MIREL 2018 workshop on MIning and REasoning with Legal texts

PB - EasyChair

Y2 - 17 September 2018 through 17 September 2018

ER -

Legal Reasoning and Big Data: Opportunities and Challenges

Abstract

Workshop

Access to Document

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Projects

MIREL: MIning and REasoning with Legal texts

Research output

Large-Scale Legal Reasoning with Rules and Databases

Cite this