Ontology Materialization by Abstraction Refinement in Horn SHOIF

Birte Glimm, Yevgeny Kazakov, Trung-Kien Tran

AAAI 2017 pp. 1114-1120

doi:10.1609/AAAI.V31I1.10691 /aaai/2017/glimm2017aaai-ontology/

Abstract

Abstraction refinement is a recently introduced technique using which reasoning over large ABoxes is reduced to reasoning over small abstract ABoxes. Although the approach is sound for any classical Description Logic such as SROIQ, it is complete only for Horn ALCHOI. In this paper, we propose an extension of this method that is now complete for Horn SHOIF and also handles role- and equality-materialization. To show completeness, we use a tailored set of materialization rules that loosely decouple the ABox from the TBox. An empirical evaluation demonstrates that, despite the new features, the abstractions are still significantly smaller than the original ontologies and the materialization can be computed efficiently.

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Cite

Text

Glimm et al. "Ontology Materialization by Abstraction Refinement in Horn SHOIF." AAAI Conference on Artificial Intelligence, 2017. doi:10.1609/AAAI.V31I1.10691

Markdown

[Glimm et al. "Ontology Materialization by Abstraction Refinement in Horn SHOIF." AAAI Conference on Artificial Intelligence, 2017.](https://mlanthology.org/aaai/2017/glimm2017aaai-ontology/) doi:10.1609/AAAI.V31I1.10691

BibTeX

@inproceedings{glimm2017aaai-ontology,
  title     = {{Ontology Materialization by Abstraction Refinement in Horn SHOIF}},
  author    = {Glimm, Birte and Kazakov, Yevgeny and Tran, Trung-Kien},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2017},
  pages     = {1114-1120},
  doi       = {10.1609/AAAI.V31I1.10691},
  url       = {https://mlanthology.org/aaai/2017/glimm2017aaai-ontology/}
}