IJCAI 2020poster0 citations

Deductive Module Extraction for Expressive Description Logics

Patrick Koopmann, Jieying Chen

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.

Knowledge Representation and Reasoning: Description Logics and Ontologies
BibTeX
@inproceedings{ijcai2020p227,
  title     = {Deductive Module Extraction for Expressive Description Logics},
  author    = {Koopmann, Patrick and Chen, Jieying},
  booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
               Artificial Intelligence, {IJCAI-20}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Christian Bessiere},
  pages     = {1636--1643},
  year      = {2020},
  month     = {7},
  note      = {Main track},
  doi       = {10.24963/ijcai.2020/227},
  url       = {https://doi.org/10.24963/ijcai.2020/227},
}
Deductive Module Extraction for Expressive Description Logics · IJCAI 2020