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In the recent years, video sharing in social media from different video recording devices has resulted in a exponential growth of videos on the Internet. Such video data is continuously increasing with daily recordings related to a wide number of topics. In this context, video understanding has become a critical problem to address. Video search and indexation benefits from the use of keyword tags related to the video content, but most of the shared video content does not contain these tags. Although the use of deep learning has become essential for image analysis in several areas, video domain is still a relatively unexplored field for these type of methods. On the other hand knowledge graphs as Freebase or WordNet store high quantities of information about the word and relations that can be used to disambiguate concepts and relate them through contextual information In this research project we search to explore and improve the understanding of video content through the use of automatic tagging models based on Machine Learning and Deep Learning techniques, improved by the use of knowledge bases., Peer Reviewed, Postprint (published version)

Today's media and news organizations are constantly generating large amounts of multimedia content, majorly delivered online. As the online media market grows, the management and delivery of contents is becoming a challenge. Computational approaches can help to overcome this challenge by governing different applications such as content creation, production, search, and its promotion and distribution to different audiences. In this abstract we present a success story of the adoption of semantic technologies on the aforementioned applications, which are built on top of a semantic tagging framework, based on a Knowledge Graph (KG). The presented pipeline combines multimodal inputs into a contextual entity linking module, which indexes documents and links them to trends and stories developing on the news. We will describe how documents are linked and provided to media producers through Vilynx's platform, which is currently indexing over 20k media documents a day., Peer Reviewed, Postprint (published version)