Deductive Module Extraction for Expressive Description Logics
Abstract
In deductive module extraction, we determine a small subset of an ontology for a given vocabulary that preserves all logical entailments that can be expressed in that vocabulary. While in the literature stronger module notions have been discussed, we argue that for applications in ontology analysis and ontology reuse, deductive modules, which are decidable and potentially smaller, are often sufficient. We present methods based on uniform interpolation for extracting different variants of deductive modules, satisfying properties such as completeness, minimality and robustness under replacements, the latter being particularly relevant for ontology reuse. An evaluation of our implementation shows that the modules computed by our method are often significantly smaller than those computed by existing methods.
Cite
Text
Koopmann and Chen. "Deductive Module Extraction for Expressive Description Logics." International Joint Conference on Artificial Intelligence, 2020. doi:10.24963/IJCAI.2020/227Markdown
[Koopmann and Chen. "Deductive Module Extraction for Expressive Description Logics." International Joint Conference on Artificial Intelligence, 2020.](https://mlanthology.org/ijcai/2020/koopmann2020ijcai-deductive/) doi:10.24963/IJCAI.2020/227BibTeX
@inproceedings{koopmann2020ijcai-deductive,
title = {{Deductive Module Extraction for Expressive Description Logics}},
author = {Koopmann, Patrick and Chen, Jieying},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2020},
pages = {1636-1643},
doi = {10.24963/IJCAI.2020/227},
url = {https://mlanthology.org/ijcai/2020/koopmann2020ijcai-deductive/}
}