Temporal reasoning on Twitter streams using semantic web technologies

There has been a significant increase in recent years in the volume and diversity of streams of data, data streams from sensors, data streams arising from the analysis of content or data mining, right through to user generated Twitter streams. There has been a corresponding increase in demand for more real-time analysis of these streams in order to spot significant events and trends of interest to an individual or business. This has resulted in an increased need to achieve efficient temporal reasoning upon the streams.

In this paper, we present a novel approach to perform temporal reasoning on real time streams of data using Semantic Web Technologies so that we could derive more valuable information by taking account of the time dimension. Moreover, in order to deal with such high-frequency data, several filter mechanisms have been implemented to, significantly, improve the performance of the reasoning process. In order to illustrate and evaluate the approach, the real-time analysis of Twitter data is taken as a concrete use case for such data streams.