Crime Knowledge Extraction: an Ontology-driven Approach for Detecting Abstract Terms in Case Law Decisions
Capitolo
Data di Pubblicazione:
2019
Citazione:
Crime Knowledge Extraction: an Ontology-driven Approach for Detecting Abstract Terms in Case Law Decisions / S. Castano, A. Ferrara, M. Falduti, S. Montanelli - In: ICAIL '19 : Proceedings[s.l] : ACM, 2019. - ISBN 9781450367547. - pp. 179-183 (( Intervento presentato al 7. convegno International Conference on Artificial Intelligence and Law tenutosi a Montreal nel 2019 [10.1145/3322640.3326730].
Abstract:
In this paper, we present CRIKE, a data-science approach to automatically detect concrete applications of legal abstract terms in case-law decisions. To this purpose, CRIKE relies on the use of the LATO ontology where legal abstract terms are properly formalized as concepts and relations among concepts. Using LATO, CRIKE aims at discovering how and where legal abstract terms are applied by judges in their legal argumentation. Moreover, we detect the terminology used in the text of case-law decisions to characterize concrete abstract-term instances. A case-study on a case-law decisions dataset provided by the Court of Milan, Italy, is also discussed.
Tipologia IRIS:
03 - Contributo in volume
Keywords:
legal ontology; legal-term extraction; case-law analysis
Elenco autori:
S. Castano, A. Ferrara, M. Falduti, S. Montanelli
Link alla scheda completa:
Titolo del libro:
ICAIL '19 : Proceedings