Stream Reasoning in Temporal Datalog

Authors

  • Alessandro Ronca University of Oxford
  • Mark Kaminski University of Oxford
  • Bernardo Cuenca Grau University of Oxford
  • Boris Motik University of Oxford
  • Ian Horrocks University of Oxford

DOI:

https://doi.org/10.1609/aaai.v32i1.11537

Keywords:

stream reasoning, temporal reasoning, datalog, query answering, stream processing

Abstract

In recent years, there has been an increasing interest in extending traditional stream processing engines with logical, rule-based, reasoning capabilities. This poses significant theoretical and practical challenges since rules can derive new information and propagate it both towards past and future time points; as a result, streamed query answers can depend on data that has not yet been received, as well as on data that arrived far in the past. Stream reasoning algorithms, however, must be able to stream out query answers as soon as possible, and can only keep a limited number of previous input facts in memory. In this paper, we propose novel reasoning problems to deal with these challenges, and study their computational properties on Datalog extended with a temporal sort and the successor function (a core rule-based language for stream reasoning applications).

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Published

2018-04-25

How to Cite

Ronca, A., Kaminski, M., Cuenca Grau, B., Motik, B., & Horrocks, I. (2018). Stream Reasoning in Temporal Datalog. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.11537

Issue

Section

AAAI Technical Track: Knowledge Representation and Reasoning