<efrbr:recordSet xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:efrbr="http://vfrbr.info/efrbr/1.1" xmlns:efrbr-work="http://vfrbr.info/efrbr/1.1/work" xmlns:efrbr-expression="http://vfrbr.info/efrbr/1.1/expression" xmlns:efrbr-manifestation="http://vfrbr.info/efrbr/1.1/manifestation" xmlns:efrbr-person="http://vfrbr.info/efrbr/1.1/person" xmlns:efrbr-corporateBody="http://vfrbr.info/efrbr/1.1/corporateBody" xmlns:efrbr-concept="http://vfrbr.info/efrbr/1.1/concept" xmlns:efrbr-structure="http://vfrbr.info/efrbr/1.1/structure" xmlns:efrbr-responsible="http://vfrbr.info/efrbr/1.1/responsible" xmlns:efrbr-subject="http://vfrbr.info/efrbr/1.1/subject" xmlns:efrbr-other="http://vfrbr.info/efrbr/1.1/other" xsi:schemaLocation="http://vfrbr.info/efrbr/1.1 http://vfrbr.info/schemas/1.1/efrbr.xsd"><efrbr:entities><efrbr-work:work identifier="http://purl.tuc.gr/dl/dias/7A6AD56D-57AE-4F14-B65C-A5945E37DA25"><efrbr-work:titleOfTheWork>Explainable reasoning with legal big data: a layered framework</efrbr-work:titleOfTheWork></efrbr-work:work><efrbr-expression:expression identifier="http://purl.tuc.gr/dl/dias/7A6AD56D-57AE-4F14-B65C-A5945E37DA25"><efrbr-expression:titleOfTheExpression>Explainable reasoning with legal big data: a layered framework</efrbr-expression:titleOfTheExpression><efrbr-expression:formOfExpression vocabulary="DIAS:TYPES">
            Peer-Reviewed Journal Publication
            Δημοσίευση σε Περιοδικό με Κριτές
         </efrbr-expression:formOfExpression><efrbr-expression:dateOfExpression type="issued">2024-03-01</efrbr-expression:dateOfExpression><efrbr-expression:dateOfExpression type="published">2022</efrbr-expression:dateOfExpression><efrbr-expression:languageOfExpression vocabulary="iso639-1">en</efrbr-expression:languageOfExpression><efrbr-expression:summarizationOfContent>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.</efrbr-expression:summarizationOfContent><efrbr-expression:useRestrictionsOnTheExpression type="creative-commons">http://creativecommons.org/licenses/by/4.0/</efrbr-expression:useRestrictionsOnTheExpression><efrbr-expression:note type="journal name">Journal of Applied Logics-IfCoLoG Journal of Logics and their Applications</efrbr-expression:note><efrbr-expression:note type="journal volume">9</efrbr-expression:note><efrbr-expression:note type="journal number">4</efrbr-expression:note><efrbr-expression:note type="page range">1155-1170</efrbr-expression:note></efrbr-expression:expression><efrbr-person:person identifier="https://viaf.org/viaf/85475721"><efrbr-person:nameOfPerson vocabulary="VIAF">
            Antoniou, Grigoris, 1961-
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            Atkinson Katie
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            Baryannis George
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            Batsakis Sotirios
            Μπατσακης Σωτηριος
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            Di Caro Luigi
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            Governatori, Guido
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            Robaldo Livio
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            Siragusa Giovanni
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            Tachmazidis Ilias
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            College Publications
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            Big data
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            Knowledge representation
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            Data storage equipment
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