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8 results for “description logic”
Empirical Investigation of Subsumption Test Hardness in Description Logic Classification
<p>This is the dataset supporting the analysis for the submission "Empirical Investigation of Subsumption Test Hardness in Description Logic Classification" to CADE, 2015. Data in its very raw form is not included, but the dataset containing non aggregated subsumption test data (labelled subsumptiontest_data) and the overall module data (labelled module_data) contain all the records corresponding to the raw data (albeit amended by some additional measurements and ontology metadata). </p> <p>The r_scripts directory contains all the<strong> r files needed to run the analysis</strong>. However, the code is written ad hoc and is very slow, and should only be considered as a reference. Running the code does not work out of the box, as a series of directory paths need to be set manually.</p> <p>The r_data directory contains all the <strong>datasets used in the analysis.</strong>.</p> <p>-- map_subsumptiontest_data_non_aggregated contains the non aggregated subsumption test data from which the first experiment, the subsumption test hardness survey, is conducted<br /> -- map_module_data_non_aggregated contains the non aggregated module data from which the first experiment, the subsumption test hardness survey, is conducted</p> <p>-- module_data_non_aggregated.RData contains the non aggregated module data from which the intra-module analysis is conducted<br /> -- subsumptiontest_data_non_aggregated.RData contains the non aggregated subsumption test data from which the intra-module analysis is conducted</p> <p>-- inter_module_data.RData contains the results of the inter-module analysis from the perspective of the modules<br /> -- intra_module_data.RData contains the results of the intra-module analysis from the perspective of the modules<br /> -- inter_subsumptiontest_data.RData contains the results of the inter-module analysis from the perspective of the subsumption tests<br /> -- intra_subsumptiontest_data.RData contains the results of the intra-module analysis from the perspective of the subsumption tests</p> <p>The csv_data directory contains the same data as the r_data directory in CSV form.</p>
Module Data for the Empirical Investigation of Subsumption Test Hardness in Description Logic Classification
<p>This dataset contains 224 modules of 14 distinct ontologies for the submission "Empirical Investigation of Subsumption Test Hardness in Description Logic Classification" to CADE, 2015. The submitted version with the dataset description can be found here:</p> <p>http://owl.cs.manchester.ac.uk/publications/supporting-material/subtest-hardness-in-classification/</p> <p>The ontologies in this set are BioPortal ontologies from the snapshot provided at http://dx.doi.org/10.5281/zenodo.15667. </p> <p>The following ontologies are in the set: </p> <p>MFOEM - Emotion Ontology<br /> BT - Biotop Ontology<br /> NEMO - Neural Electromagnetic Ontology<br /> NTDO - Neglected Tropical Disease Ontology<br /> OBI - Ontology for Biomedical Investigations<br /> OGSF - Ontology for Genetic Susceptibility Factor<br /> ONL-MSA - Mental State Assemssment<br /> VSO - Vital Sign Ontology<br /> CAO - Clusters of Orthologous Groups Cog Analysis Ontology <br /> ICO - Informed Consent Ontology<br /> OMRSE - Ontology of Medically Related Social Entities<br /> STATO - Statistics Ontology<br /> NPO - Nanoparticle Ontology<br /> OBCS - Ontology of Biological and Clinical Statistics</p>
Evonne: Interactive Proof Visualization for Description Logics (System Description) - IJCAR22 - Resources
<p>- <strong>evonne-experiments-ijcar22.zip</strong>: contains data and scripts used in the experiments.</p> <p>- <strong>evonne-tool-ijcar22.zip: </strong>contains the version of Evonne described in the paper.</p> <p>For more about Evonne (latest version, publications), visit <a href="https://imld.de/evonne">https://imld.de/evonne</a></p>
Explaining Non-Entailment by Model Transformation for the Description Logic EL - IJCKG22 - Resources
<p><strong>resources.zip</strong> contains the experiment data and results, and a README file explaining how to rerun the experiment.</p>
Absorption-Based Query Answering for Expressive Description Logics : Evaluation Data
<p>Sources ( <code>Sources.zip</code> ), evaluation data ( <code>Evaluation.zip</code> ) with all ontologies and queries, as well as the evaluation results ( <code>Results.zip</code> ) of the publication "<em>Absorption-Based Query Answering for Expressive Description Logics</em>" from the 18th International Semantic Web Conference (ISWC), October 26-30, 2019, Auckland, New Zealand. For easily reproducing the evaluation, you can also use the Docker image <em>koncludeeval/abqa</em> (available at <a href="https://hub.docker.com/r/koncludeeval/abqa">https://hub.docker.com/r/koncludeeval/abqa</a>). See also readme files in archives for more details.</p> <p>You may only use/share/etc. the included reasoners/ontologies as restricted by their licences (see <code>Sources.zip</code>). In particular, Konclude and OWL BGP are available under the LGPLv3 (<a href="https://www.gnu.org/licenses/lgpl-3.0.en.html">https://www.gnu.org/licenses/lgpl-3.0.en.html</a>), Pellet under the AGPLv3 (<a href="https://www.gnu.org/licenses/agpl-3.0.en.html">https://www.gnu.org/licenses/agpl-3.0.en.html</a>), and PAGOdA under an Academic Licence (<a href="http://www.cs.ox.ac.uk/isg/tools/PAGOdA/PAGOdA_Academic_Licence.txt">http://www.cs.ox.ac.uk/isg/tools/PAGOdA/PAGOdA_Academic_Licence.txt</a>).</p>
Combining Proofs for Description Logic and Concrete Domain Reasoning - RuleML+RR23 - Resources
<p><strong>experiment-RuleML+RR-2023.zip</strong> contains the materials used in the experiment described in the paper, and a README file with instructions on how to rerun the experiment.</p>
Axiom Pinpointing for Lightweight Description Logics through Incremental SAT
<p>This repository contains results of empirical evaluation of the procedure described in article SATPin: Axiom Pinpointing for Lightweight Description Logics through Incremental SAT, and input for this evaluation.</p>
Finding Small Proofs for Description Logic Entailments: Theory and Practice - LPAR20 - Resources
<p><strong>experiments-LPAR-2020 </strong>contains all datasets and scripts used in the experiments described in the paper, and a README file with instructions on how to rerun the experiments.</p>
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