TellMeFirst – A Knowledge Discovery Application

Executive summary

Background

Objectives

TMF intends to leverage Linked Data and NLP technologies to extract the main topics from texts in the form of DBpedia resources, retrieving new information from the Web. In the previous years, we have created a structured and well-defined process to maintain the training set updated: this is a necessary step for classifying documents concerning recent topics. The next step was the development of a module for building ad hoc training sets for documents related to a specific area of knowledge. For these reasons, we have focused on the development of a parametrized process in order to adapt TellMeFirst to different purposes and different semantic areas. Through the development of this feature, TMF can be exploited by companies, public administrations, and cultural institution that need a classification system for their specific knowledge domains and purposes. The TMF software has now reached maturity and therefore we will explore use cases of the tool within structured projects.

Results

Related Publications

2015

Rocha, Oscar Rodriguez; Vagliano, Iacopo; Martinez, Cristhian Nicolas Figueroa; Cairo, Federico; Futia, Giuseppe; Licciardi, Carlo; Marengo, Marco; Morando, Federico

Semantic Annotation and Classification in Practice Journal Article

In: IT PROFESSIONAL, vol. 17, no. IT-Enabled Business Innovation, pp. 33–39, 2015.

Abstract | Links | BibTeX

2014

Futia, Giuseppe; Cairo, Federico; Morando, Federico; Leschiutta, Luca

Exploiting Linked Data and Natural Language Processing for the Classification of Political Speech Conference

Conference for E-Democracy and Open Governement 2014.

Abstract | Links | BibTeX