The dynamic deployment of Information and Communication Technologies in the judicial field, together with the dematerialization of proceedings pushed by e-justice plans, is encouraging the introduction of novel litigation support systems. In this paper we present two innovative systems, JUMAS and eJRM, which take up the challenge of exploiting semantics and machine learning techniques for managing in-court and out-of-court proceedings respectively. JUMAS stems from the homonymous EU research project ended in 2011. It provides not only a streamlined content creation and management support for acquiring and sharing the knowledge embedded into judicial folders, but also a semantic enrichment of multimedia data towards a better usability of judicial folders. eJRM arises from the related ongoing research project funded in the framework PON “Ricerca e Competitività 2007-2013”. It exploits semantic representation and machine learning reasoning mechanisms towards a support system for online mediation to encourage the resolution of out-of-court disputes and consequently to increase access to justice.

Fersini, E., Archetti, F., Messina, V. (2013). Towards a Smooth E-Justice: Semantic Models and Machine Learning. In M. Fathi (a cura di), Integration of Practice-Oriented Knowledge Technology: Trends and Prospectives (pp. 57-70). Springer Berlin Heidelberg [10.1007/978-3-642-34471-8_5].

Towards a Smooth E-Justice: Semantic Models and Machine Learning

FERSINI, ELISABETTA;ARCHETTI, FRANCESCO ANTONIO;MESSINA, VINCENZINA
2013

Abstract

The dynamic deployment of Information and Communication Technologies in the judicial field, together with the dematerialization of proceedings pushed by e-justice plans, is encouraging the introduction of novel litigation support systems. In this paper we present two innovative systems, JUMAS and eJRM, which take up the challenge of exploiting semantics and machine learning techniques for managing in-court and out-of-court proceedings respectively. JUMAS stems from the homonymous EU research project ended in 2011. It provides not only a streamlined content creation and management support for acquiring and sharing the knowledge embedded into judicial folders, but also a semantic enrichment of multimedia data towards a better usability of judicial folders. eJRM arises from the related ongoing research project funded in the framework PON “Ricerca e Competitività 2007-2013”. It exploits semantic representation and machine learning reasoning mechanisms towards a support system for online mediation to encourage the resolution of out-of-court disputes and consequently to increase access to justice.
Capitolo o saggio
machine learning; semantics; e-justice; integrated systems
English
Integration of Practice-Oriented Knowledge Technology: Trends and Prospectives
Fathi, M
2013
978-3-642-34470-1
Springer Berlin Heidelberg
57
70
Fersini, E., Archetti, F., Messina, V. (2013). Towards a Smooth E-Justice: Semantic Models and Machine Learning. In M. Fathi (a cura di), Integration of Practice-Oriented Knowledge Technology: Trends and Prospectives (pp. 57-70). Springer Berlin Heidelberg [10.1007/978-3-642-34471-8_5].
none
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/43218
Citazioni
  • Scopus 3
  • ???jsp.display-item.citation.isi??? ND
Social impact