Deep Neural Networks for Approximating Stream Reasoning with C-SPARQL

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationProgress in Artificial Intelligence - 20th EPIA Conference on Artificial Intelligence, EPIA 2021, Proceedings
EditorsGoreti Marreiros, Francisco S. Melo, Nuno Lau, Henrique Lopes Cardoso, Luís Paulo Reis
Place of PublicationCham
Number of pages13
ISBN (Electronic)978-3-030-86230-5
ISBN (Print)978-3-030-86229-9
Publication statusPublished - 2021
Event20th EPIA Conference on Artificial Intelligence, EPIA 2021 - Virtual, Online
Duration: 7 Sept 20219 Sept 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12981 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference20th EPIA Conference on Artificial Intelligence, EPIA 2021
CityVirtual, Online


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