MeSoOnTV: A Media and Social-driven Ontology-based TV Knowledge Management System

Searching, browsing and analyzing web contents is today a challenging problem when compared to early Internet ages. This is due to the fact that web content is multimedial, social and dynamic. Moreover, concepts referred by videos, news, comments, posts, are implicitly linked by the fact that people on the Web talks about something, somewhere at some time and these connections may change as the perception of users on the Web changes over time. We define a model for the integration of the heterogeneous and dynamic data coming from different knowledge sources (broadcasters' archives, online newspapers, blogs, web encyclopedias, social media platforms, social networks, etc.). We use a knowledge graph to model all the heterogenous aspects of the information in an homogeneous way. Through a case study on social TV, we provide a non trivial cross-domain analysis scenario on real data gathered from YouTube and Twitter, and related to an Italian TV talk show on politics, broadcasted by RAI, the Italian public-service broadcasting organization.