Semantics and Machine Learning for Building the Next Generation of Judicial Court Management Systems

Information and Communication Technologies play a fundamental role in e-justice: the traditional judicial folder is being transformed into an integrated multimedia folder, where documents, audio and video recordings can be accessed and searched via web-based judicial content management platforms. Usability of the electronic judicial folders is still hampered by traditional support toolset, allowing search only in textual information, rather than directly in audio and video recordings. Transcription of audio recordings and template filling are still largely manual activities. Thus a significant part of the information available in the trial folder is usable only through a time consuming manual search especially for audio and video recordings that describe not only what was said in the courtroom, but also the way and the specific trial context in which it was said. In this paper we present the JUMAS system, stemming from the TUMAS project started on February 2008, that takes up the challenge of using semantics towards a better usability of the multimedia judicial folders. The main aim of this paper is to show how JUMAS has provided the judicial users with a powerful toolset able to fully exploit the knowledge embedded into multimedia judicial folders