A semantic approach for document classification using deep neural networks and multimedia knowledge graph

The amount of available multimedia data in different formats and from different sources increases everyday. From an information retrieval point of view, this high volume and heterogeneity of data involves several issues to be addressed related to information overload and lacks of well structured information. Even if modern information retrieval systems offer to the user manifold search options, it is still hard to find systems with optimal performances in the document seeking process starting from a given topic. In recent years, several frameworks have been proposed and developed to support this task based on different models and techniques. In this paper we propose a semantic approach to document classification using both textual and visual topic detection techniques based on deep neural networks and multimedia knowledge graph. A semantic multimedia knowledge base has been exploited and several experimental results show the effectiveness of our proposed approach. • A combination of textual and visual information is suitable for topic detection. • Semantic analysis and deep neural networks are used in our approach. • Knowledge is represented by a Multimedia Knowledge Graph. • A web document collection is a real scenario to test our strategy.