User-centered Information Retrieval using Semantic Multimedia Big Data

The user is a basic component in the whole information management process and for this reason s/he has to be taken into account in the design and implementation of frameworks and systems for information retrieval. For this reason, the problem of defining efficient techniques for knowledge representation in a user's prospective is becoming a challenging topic in both academic and industrial community. Moreover, the large amount of available data induces several problems from different points of view and it is stressed in the bigdata vision. In this latter some approaches related to the semantic web could be used to improve the data models which underlie the implementation of user-based information retrieval applications. In this paper we propose the use of a semantic approach to design the structure of a multimedia BigData. In addition, the recognition of multimodal features to represent concepts and its attributes together with linguistic properties to relate them are an effective way to bridge the gap between the target semantic classes and the available low-level multimedia descriptors. Information about users (i.e. position) is also used to recognize his/her behaviour and improve the quality of the information retrieval process. Our framework has been implemented using a NoSQL graphdb populated from very large knowledge sources and mobile technologies. Extended experiments are presented to show the effectiveness of our approach.