Computing Horn Rewritings of Description Logics Ontologies
Authors: Mark Kaminski, Bernardo Cuenca Grau
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments indicate that many real-world ontologies satisfy our sufficient conditions and thus admit polynomial Horn rewritings. We have implemented markability checking and evaluated our techniques on a large ontology repository. Our results indicate that many real-world ontologies are markable and thus admit Horn rewritings of polynomial size. |
| Researcher Affiliation | Academia | Mark Kaminski and Bernardo Cuenca Grau Department of Computer Science, University of Oxford, UK |
| Pseudocode | No | The paper provides formal definitions and rules but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states that the authors have implemented markability checking and evaluated their techniques, but it does not provide any concrete access (link or explicit statement of release) to the source code for the methodology described. |
| Open Datasets | Yes | We analysed 120 non-Horn ontologies extracted from the Protege Ontology Library, Bio Portal (http://bioportal.bioontology.org/), the corpus by Gardiner et al. [2006], and the EBI linked data platform (http://www.ebi.ac.uk/rdf/platform). |
| Dataset Splits | No | The paper mentions analyzing a set of ontologies but does not specify any training, validation, or test dataset splits, nor does it refer to predefined splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper mentions implementing "the 2-SAT reduction in Section 4 and a simple 2-SAT solver" but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | The paper describes the theoretical framework and transformations but does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings. |