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(to be completed)

 


ThinkMind // International Journal On Advances in Software, volume 7, numbers 1 and 2, 2014 // View article soft_v7_n12_2014_5


A Detailed Description of the EC2M Project: Exploiting Ontologies for the Automatic and Manual Documents Classification in Industrial Enterprise Content Management Systems

Authors:
Daniela Briola
Alessandro Amicone

Keywords: Ontologies, Semantic Classification, Knowledge Representation, Industrial Application, Automatic Documents Classification

Abstract:
Enterprise Content Management (ECM) systems represent a crucial aspect in the efficient and effective management of large-scale enterprises, in particular for those made up of several sites distributed all over the world. The increasing number of documents to be managed, the problems related to the sharing of private information between commercial partners, the need for semantically describing the contents of shared documents have pushed researchers to find new techniques and solutions to deal with these challenges. We already presented the high level description of a joint project of the Department of Informatics, Bioengineering, Robotics and System Engineering of the University of Genoa, Italy, and two companies, Nacon (member of Sempla Group, now part of the GFT Group) and Nis, to create an improved ECM system (named EC2M) exploiting ontologies to better classify, retrieve and share documentation among different sites of the involved companies: in this paper, we give a more detailed description of the project, with respect to its modules and to the underlying ontology used to classify documents. We present the automatic documents classification algorithm too, with an example of its execution. The developed system, which was born from a real industrial need, is currently used by GFT Italy to manage and share its documents among more than 600 users distributed in many different geographical locations and, thanks to the ontology, the semantic tagging process and the automatic documents forwarding have been successfully achieved. This joint project proves how a more formal representation of the documents domain can effectively improve the standard way of classifying and retrieving documents in real industrial scenarios, representing a winning collaboration between university and industry.

Pages: 63 to 76

Copyright: Copyright (c) to authors, 2014. Used with permission.

Publication date: June 30, 2014

Published in: journal

ISSN: 1942-2628

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