This work describes the developement and the realization of an OSInt solution conducting a supplier risk assessement, focused on the evaluation of suppliers’ reputation starting from publicity available information. The main challenge is represented by the data processing phase that exploits NLP technologies to extract facts, events, and relations from unstructured sources, building the knowledge base for reputational analysis. Several measures have been adopted to provide a satisfactory user experience; however, further integrations are still needed to increase efficiency of the developed solution. Particulary, it is necessary to deepen and improve the analysis over the huge volume of data coming from open sources, enhancing the discovery of all possible relevant information influencing the reputation of the targeted entity.
Evento: KomIS2017 – International Conferenze on Data Management Technoligies and Application, 2017, Madrid (Spagna)
Autori: Raffaele Palmieri, Vincenzo Orabona, Nadia Cinque, Stefano Tangorra, Donato Cappetta.
Data: 2017
