Ontology Approximation in Horn Description Logics
Authors: Anneke Bötcher, Carsten Lutz, Frank Wolter
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the approximation of a description logic (DL) ontology in a less expressive DL, focussing on the case of Horn DLs. It is common to construct such approximations in an ad hoc way in practice and the resulting incompleteness is typically neither analyzed nor understood. In this paper, we show how to construct complete approximations. These are typically infinite or of excessive size and thus cannot be used directly in applications, but our results provide an important theoretical foundation that enables informed decisions when constructing incomplete approximations in practice. |
| Researcher Affiliation | Academia | 1University of Bremen, Germany 2University of Liverpool, UK {anneke, clu}@uni-bremen.de, wolter@liverpool.ac.uk |
| Pseudocode | No | The paper describes algorithms and constructions in natural language and logical notation, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper links to an appendix for proof details, but there is no mention or link for open-source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper and does not describe experiments using datasets. |
| Dataset Splits | No | This is a theoretical paper and does not describe experiments using datasets. |
| Hardware Specification | No | This is a theoretical paper and does not describe hardware specifications for experiments. |
| Software Dependencies | No | This is a theoretical paper and does not list specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not describe an experimental setup with hyperparameters or system-level training settings. |