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Taking stock of legal ontologies: a feature-based comparative analysis
- Source :
- Artificial Intelligence and Law, Artificial Intelligence and Law, Springer Verlag, 2020, 28 (2), pp.207-235. ⟨10.1007/s10506-019-09252-1⟩, Artificial Intelligence and Law, 2020, 28 (2), pp.207-235. ⟨10.1007/s10506-019-09252-1⟩
- Publication Year :
- 2020
- Publisher :
- HAL CCSD, 2020.
-
Abstract
- International audience; Ontologies represent the standard way to model the knowledge about specific domains. This holds also for the legal domain where several ontologies have been put forward to model specific kinds of legal knowledge. Both for standard users and for law scholars, it is often difficult to have an overall view on the existing alternatives, their main features and their interlinking with the other ontologies. To answer this need, in this paper, we address an analysis of the state-of-the-art in legal ontologies and we characterise them along with some distinctive features. This paper aims to guide generic users and law experts in selecting the legal ontology that better fits their needs and in understanding its specificity so that proper extensions to the selected model could be investigated.
- Subjects :
- Modelling legal knowledge
Legal ontologie
Computer science
Legal ontologies
Semantic web
[SCCO.COMP]Cognitive science/Computer science
Legal aspects of computing
Legal ontology
Legal knowledge
ComputingMilieux_LEGALASPECTSOFCOMPUTING
02 engineering and technology
Legal domain
Data science
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Artificial Intelligence
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
Feature based
020201 artificial intelligence & image processing
Philosophy of law
Law
Semantic Web
Stock (geology)
Subjects
Details
- Language :
- English
- ISSN :
- 09248463 and 15728382
- Database :
- OpenAIRE
- Journal :
- Artificial Intelligence and Law, Artificial Intelligence and Law, Springer Verlag, 2020, 28 (2), pp.207-235. ⟨10.1007/s10506-019-09252-1⟩, Artificial Intelligence and Law, 2020, 28 (2), pp.207-235. ⟨10.1007/s10506-019-09252-1⟩
- Accession number :
- edsair.doi.dedup.....068e2e38d89d0fd66349369c71d6032f
- Full Text :
- https://doi.org/10.1007/s10506-019-09252-1⟩