Efficient Computation of General Modules for ALC Ontologies
Authors: Hui Yang, Patrick Koopmann, Yue Ma, Nicole Bidoit
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our evaluation indicates that our general modules are often smaller than classical modules and uniform interpolants computed by the state-of-the-art, and compared with uniform interpolants, can be computed in a significantly shorter time. Our evaluation shows that all our methods, including the one for uniform interpolation, can compete with the run times of locality-based module extraction, while at the same time resulting in subtantially smaller ontologies (Section 7). |
| Researcher Affiliation | Academia | Hui Yang1 , Patrick Koopmann2 , Yue Ma1 and Nicole Bidoit1 1LISN, CNRS, Universit e Paris-Saclay 2 Vrije Universiteit Amsterdam, The Netherlands |
| Pseudocode | Yes | The paper includes 'Figure 2: Inference rules for computing DΣ(O)' and 'Figure 3: Rules to eliminate definers', which present structured steps and rules for the methods described. |
| Open Source Code | Yes | A prototype of our method can be found at https://hub.docker.com/r/ yh1997/demo gemo. |
| Open Datasets | Yes | The ontologies used in our experiment are generated from the OWL Reasoner Evaluation (ORE) 2015 classification track [Parsia et al., 2017] |
| Dataset Splits | No | The paper mentions generating 50 signatures for each ontology and using the OWL Reasoner Evaluation (ORE) 2015 classification track, but it does not provide specific details on training/validation/test dataset splits for reproducibility in the traditional sense of machine learning models. |
| Hardware Specification | Yes | All the experiments were performed on a machine with an Intel Xeon Silver 4112 2.6GHz, 64 Gi B of RAM, Ubuntu 18.04, and Open JDK 11. |
| Software Dependencies | Yes | We implemented a prototype called GEMO in Python 3.7.4. All the experiments were performed on a machine with an Intel Xeon Silver 4112 2.6GHz, 64 Gi B of RAM, Ubuntu 18.04, and Open JDK 11. We compared our methods with four different alternatives: (i) -modules [Grau et al., 2008] as implemented in the OWL API [Horridge and Bechhofer, 2011]; (iii) LETHE 0.61[Koopmann, 2020] and FAME 1.02 [Zhao and Schmidt, 2018] that compute uniform interpolants. |
| Experiment Setup | Yes | For each ontology, we generated 50 signatures consisting of 100 concept and role names. As in [Koopmann and Chen, 2020], we selected each concept/role name with a probability proportional to their occurrence frequency in the ontology. We set a time limit of 10s for this task. We say a method succeeds on a request if it outputs the expected results within 600s. |