As distributed IoT applications become larger and more complex, the simple processing of raw sensor and actuation data streams becomes impractical. Instead, data streams must be fused into tangible facts and these pieces of information must be combined with a background knowledge to infer new bits of knowledge. And since many IoT applications require almost real-time reactivity to stimuli from the environment this information inference process has to be performed in a continuous, on-line manner. This paper proposes a new semantic model for data stream processing and real-time symbolic reasoning based on the concepts of Semantic Stream and Fact Stream, as a natural extensions of Complex Event Processing (CEP) and RDF (graph-based knowledge model). The main advantages of our approach are that: (a) it considers time as a key relation between pieces of information; (b) the processing of streams can be implemented using CEP and that (c) it is general enough to be applied to any Data Stream Management System (DSMS).