Similar challenges have been identified by S. Auer and J. Lehmann [37]. Unlike [36], [37] proposes solutions for some of these challenges (data integration, scalable reasoning, etc.). Semantics could be considered as a magical world to bridge
the gap of the hétérogénéity of data. Moreover, semantics can be used in a decidable system which makes possible to detect inconsistency of data, generates new knowledge using inference engine or simply links more accurately specific data not relevant for machine learning based techniques. In the literature, we can find work whose purpose is about the challenges mentioned before. Before presenting them, we must note that the relation between Big Data and semantics is bidirectional.