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Knowledge evolution is a major challenge in knowledge management. When knowledge evolution is conveyed in the form of data streams, a combined approach of deductive and inductive reasoning can leverage the clear separation between the evolving (streaming) and the static parts of the knowledge at the conceptual and technological level. In particular, the notion of RDF streams is the “glue” for the interoperability of inductive and deductive reasoning techniques with methods and systems for processing data streams. In this paper, we show our combined approach applied to social network analysis, we give experimental evidence of good performances, and demonstrate the effectiveness of the approach for extracting trends from micro-blogs and feeds.