Frontiers and Exact Learning of ELI Queries Under DL-Lite Ontologies
Abstract
We study ELI queries (ELIQs) in the presence of ontologies formulated in the description logic DL-Lite. For the dialect DL-LiteH, we show that ELIQs have a frontier (set of least general generalizations) that is of polynomial size and can be computed in polynomial time. In the dialect DL-LiteF, in contrast, frontiers may be infinite. We identify a natural syntactic restriction that enables the same positive results as for DL-LiteH. We use our results on frontiers to show that ELIQs are learnable in polynomial time in the presence of a DL-LiteH / restricted DL-LiteF ontology in Angluin's framework of exact learning with only membership queries.
Cite
Text
Funk et al. "Frontiers and Exact Learning of ELI Queries Under DL-Lite Ontologies." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/364Markdown
[Funk et al. "Frontiers and Exact Learning of ELI Queries Under DL-Lite Ontologies." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/funk2022ijcai-frontiers/) doi:10.24963/IJCAI.2022/364BibTeX
@inproceedings{funk2022ijcai-frontiers,
title = {{Frontiers and Exact Learning of ELI Queries Under DL-Lite Ontologies}},
author = {Funk, Maurice and Jung, Jean Christoph and Lutz, Carsten},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2022},
pages = {2627-2633},
doi = {10.24963/IJCAI.2022/364},
url = {https://mlanthology.org/ijcai/2022/funk2022ijcai-frontiers/}
}