Small Is Beautiful: Computing Minimal Equivalent EL Concepts
Abstract
In this paper, we present an algorithm and a tool for computing minimal, equivalent EL concepts wrt. a given ontology. Our tool can provide valuable support in manual development of ontologies and improve the quality of ontologies automatically generated by processes such as uniform interpolation, ontology learning, rewriting ontologies into simpler DLs, abduction and knowledge revision. Deciding whether there exist equivalent EL concepts of size less than k is known to be an NP-complete problem. We propose a minimisation algorithm that achieves reasonable computational performance also for larger ontologies and complex concepts. We evaluate our tool on several bio-medical ontologies with promising results.
Cite
Text
Nikitina and Koopmann. "Small Is Beautiful: Computing Minimal Equivalent EL Concepts." AAAI Conference on Artificial Intelligence, 2017. doi:10.1609/AAAI.V31I1.10684Markdown
[Nikitina and Koopmann. "Small Is Beautiful: Computing Minimal Equivalent EL Concepts." AAAI Conference on Artificial Intelligence, 2017.](https://mlanthology.org/aaai/2017/nikitina2017aaai-small/) doi:10.1609/AAAI.V31I1.10684BibTeX
@inproceedings{nikitina2017aaai-small,
title = {{Small Is Beautiful: Computing Minimal Equivalent EL Concepts}},
author = {Nikitina, Nadeschda and Koopmann, Patrick},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2017},
pages = {1206-1212},
doi = {10.1609/AAAI.V31I1.10684},
url = {https://mlanthology.org/aaai/2017/nikitina2017aaai-small/}
}