Deductive Module Extraction for Expressive Description Logics
Authors: Patrick Koopmann, Jieying Chen
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implemented a prototype of our approach which we evaluate and compare with existing methods, both for computing random modules and atomic decompositions, a structure representing all self-contained modules of an ontology. We evaluated our method on ontologies in the DL Classification track of the OWL Reasoner Evaluation (ORE) 2015 [Parsia et al., 2017]. Statistics on the sizes of modules are shown in Table 1. |
| Researcher Affiliation | Academia | 1Institute for Theoretical Computer Science, Technische Universit at Dresden, Germany 2SIRIUS Centre, Department of Informatics, University of Oslo, Norway |
| Pseudocode | Yes | Algorithm 1 Computing minimal deductive modules Input: ontology O, signature Σ, integer n Output: a minimal deductive module M |
| Open Source Code | Yes | We implemented a prototype of our approach which we evaluate and compare with existing methods, both for computing random modules and atomic decompositions, a structure representing all self-contained modules of an ontology. For this, we implemented the calculus consisting of the rules in Figures 1 and 2, as well as the module extraction techniques, as extension to the uniform interpolation tool LETHE [Koopmann, 2020].1 https://lat.inf.tu-dresden.de/ koopmann/LETHE (version 0.65) |
| Open Datasets | Yes | We evaluated our method on ontologies in the DL Classification track of the OWL Reasoner Evaluation (ORE) 2015 [Parsia et al., 2017]. |
| Dataset Splits | No | The paper mentions evaluating on a corpus with randomly generated signatures, but it does not specify explicit training, validation, or test dataset splits for the ontology data itself. |
| Hardware Specification | Yes | All experiments were performed on an Intel Core i5-4590 CPU with 3.30GHz and 32 GB RAM. |
| Software Dependencies | No | For checking entailments, we used the reasoner Hermi T [Glimm et al., 2014]. However, no version number for HermiT is provided, which is required for a reproducible description of ancillary software. |
| Experiment Setup | Yes | We used a timeout of 5 minutes for the uniform interpolation procedure, and a timeout of 10 minutes in complete. To evaluate the performance of our method for small modules, we randomly generated 60 signatures per ontology of 100 names each, including both concept and role names, where each name was selected with a probability proportional to its number of occurrences in the ontology. |