Deep Neural Networks for Approximating Stream Reasoning with C-SPARQL
This addresses the need for timely risk assessment in scenarios like monitoring systems by providing a faster alternative to stream reasoning, though it is an incremental application of existing neural methods to a known bottleneck.
The paper tackles the problem of C-SPARQL's inability to answer queries in real-time with large data streams by approximating reasoning using Recurrent Neural Networks and Convolutional Neural Networks, achieving high accuracies and improving processing time by several orders of magnitude.
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.