Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/2070
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dc.contributor.authorOconitrillo L.R.R.en_US
dc.contributor.authorVargas J.J.en_US
dc.contributor.authorCamacho Aen_US
dc.contributor.authorBurgos A.en_US
dc.contributor.authorCorchado J.M.en_US
dc.contributor.authorKamaruddin M.en_US
dc.contributor.authorWan-Arfah, Nen_US
dc.contributor.authorNaing, NNen_US
dc.contributor.authorAbu Hassan, MRen_US
dc.contributor.authorChan, HKen_US
dc.contributor.authorHasan, H.en_US
dc.date.accessioned2021-12-15T09:12:33Z-
dc.date.available2021-12-15T09:12:33Z-
dc.date.issued2021-
dc.identifier.issn1860949X-
dc.identifier.urihttp://hdl.handle.net/123456789/2070-
dc.descriptionScopusen_US
dc.description.abstractThe need for studies connecting the machine’s explainability with granularity is very important, especially for a detailed understanding of how data is fragmented and processed according to the domain of discourse. We develop a system called RYEL based on subject-matter experts about the legal case process, facts, pieces of evidence, and how to analyze the merits of a case. Through this system, we study the Explainable Artificial Intelligence (XAI) approach using Knowledge Graphs (KG) and enforcement unsupervised algorithms which results are expressed in an Explanatory Graphical Interface (EGI). The evidence and facts of a legal case are represented as knowledge graphs. Granular Computing (GrC) techniques are applied in the graph when processing nodes and edges using object types, properties, and relations. Through RYEL we propose new definitions for Explainable Artificial Intelligence (XAI) and Interpretable Artificial Intelligence (IAI) in a much better way and will help us to cover a technological spectrum that has not yet been covered and promises to be a new area of study which we call Interpretation-Assessment/Assessment-Interpretation (IA-AI) that consists not only in explaining machine inferences but the interpretation and assessment from a user according to a context. It is proposed a new focus-centered organization in which the XAI-IAI will be able to work and will allow us to explain in more detail the method implemented by RYEL. We believe our system has an explanatory and interpretive nature and could be used in other domains of discourse, some examples are: (1) the interpretation a doctor has about a disease and the assessment of using certain medicine, (2) the interpretation a psychologist has from a patient and the assessment for a psychological application treatment, (3) or how a mathematician interprets a real-world problem and makes an assessment about which mathematical formula to use. However, now we focus on the legal domain.en_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofStudies in Computational Intelligenceen_US
dc.subjectCase-Based Reasoning (CBR)en_US
dc.subjectExplainable Artificial Intelligence (XAI)en_US
dc.subjectExplainable legal knowledge representationen_US
dc.subjectExplanatory Graphical Interface (EGI)en_US
dc.subjectGranular Computing (GrC)en_US
dc.subjectInterpretation-Assessment/Assessment-Interpretation (IA-AI)en_US
dc.subjectRYELen_US
dc.titleRYEL System: A Novel Method for Capturing and Represent Knowledge in a Legal Domain Using Explainable Artificial Intelligence (XAI) and Granular Computing (GrC)en_US
dc.typeNationalen_US
dc.identifier.doi10.1007/978-3-030-64949-4_12-
dc.description.page369 - 399en_US
dc.volume937en_US
dc.title.titleofbookStudies in Computational Intelligenceen_US
dc.description.typeChapter in Booken_US
item.languageiso639-1en-
item.openairetypeNational-
item.grantfulltextnone-
item.fulltextNo Fulltext-
crisitem.author.deptUniversity Malaysia Kelantan, Malaysia-
crisitem.author.deptUniversiti Malaysia Kelantan-
Appears in Collections:Book Sections (Scopus Indexed) - FKP
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