Embracing Change by Abstraction Materialization Maintenance for Large ABoxes
Abstract
Abstraction Refinement is a recently introduced technique which allows for reducing materialization of an ontology with a large ABox to materialization of a smaller (compressed) `abstraction' of this ontology. In this paper, we show how Abstraction Refinement can be adopted for incremental ABox materialization by combining it with the well-known DRed algorithm for materialization maintenance. Such a combination is non-trivial and to preserve soundness and completeness, already Horn ALCHI requires more complex abstractions. Nevertheless, we show that significant benefits can be obtained for synthetic and real-world ontologies.
Cite
Text
Brenner and Glimm. "Embracing Change by Abstraction Materialization Maintenance for Large ABoxes." International Joint Conference on Artificial Intelligence, 2018. doi:10.24963/IJCAI.2018/244Markdown
[Brenner and Glimm. "Embracing Change by Abstraction Materialization Maintenance for Large ABoxes." International Joint Conference on Artificial Intelligence, 2018.](https://mlanthology.org/ijcai/2018/brenner2018ijcai-embracing/) doi:10.24963/IJCAI.2018/244BibTeX
@inproceedings{brenner2018ijcai-embracing,
title = {{Embracing Change by Abstraction Materialization Maintenance for Large ABoxes}},
author = {Brenner, Markus and Glimm, Birte},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2018},
pages = {1767-1773},
doi = {10.24963/IJCAI.2018/244},
url = {https://mlanthology.org/ijcai/2018/brenner2018ijcai-embracing/}
}