  The amount of information produced, whether by newspapers, blogs and social
networks, or by monitoring systems, is increasing rapidly. Processing all this
data in real-time, while taking into consideration advanced knowledge about the
problem domain, is challenging, but required in scenarios where assessing
potential risks in a timely fashion is critical. C-SPARQL, a language for
continuous queries over streams of RDF data, is one of the more prominent
approaches in stream reasoning that provides such continuous inference
capabilities over dynamic data that go beyond mere stream processing. However,
it has been shown that, in the presence of huge amounts of data, C-SPARQL may
not be able to answer queries in time, in particular when the frequency of
incoming data is higher than the time required for reasoning with that data. In
this paper, we investigate whether reasoning with C-SPARQL can be approximated
using Recurrent Neural Networks and Convolutional Neural Networks, two neural
network architectures that have been shown to be well-suited for time series
forecasting and time series classification, to leverage on their higher
processing speed once the network has been trained. We consider a variety of
different kinds of queries and obtain overall positive results with high
accuracies while improving processing time often by several orders of
magnitude.
