Ontology-Based Data Access with a Horn Fragment of Metric Temporal Logic

Sebastian Brandt, Elem Güzel Kalayci, Roman Kontchakov, Vladislav Ryzhikov, Guohui Xiao, Michael Zakharyaschev

AAAI 2017 pp. 1070-1076

doi:10.1609/AAAI.V31I1.10696 /aaai/2017/brandt2017aaai-ontology/

Abstract

We advocate datalogMTL, a datalog extension of a Horn fragment of the metric temporal logic MTL, as a language for ontology-based access to temporal log data. We show that datalogMTL is EXPSPACE-complete even with punctual intervals, in which case MTL is known to be undecidable. Nonrecursive datalogMTL turns out to be PSPACE-complete for combined complexity and in AC0 for data complexity. We demonstrate by two real-world use cases that nonrecursive datalogMTL programs can express complex temporal concepts from typical user queries and thereby facilitate access to log data. Our experiments with Siemens turbine data and MesoWest weather data show that datalogMTL ontology-mediated queries are efficient and scale on large datasets of up to 11GB.

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Cite

Text

Brandt et al. "Ontology-Based Data Access with a Horn Fragment of Metric Temporal Logic." AAAI Conference on Artificial Intelligence, 2017. doi:10.1609/AAAI.V31I1.10696

Markdown

[Brandt et al. "Ontology-Based Data Access with a Horn Fragment of Metric Temporal Logic." AAAI Conference on Artificial Intelligence, 2017.](https://mlanthology.org/aaai/2017/brandt2017aaai-ontology/) doi:10.1609/AAAI.V31I1.10696

BibTeX

@inproceedings{brandt2017aaai-ontology,
  title     = {{Ontology-Based Data Access with a Horn Fragment of Metric Temporal Logic}},
  author    = {Brandt, Sebastian and Kalayci, Elem Güzel and Kontchakov, Roman and Ryzhikov, Vladislav and Xiao, Guohui and Zakharyaschev, Michael},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2017},
  pages     = {1070-1076},
  doi       = {10.1609/AAAI.V31I1.10696},
  url       = {https://mlanthology.org/aaai/2017/brandt2017aaai-ontology/}
}