Efficient Extraction of EL-Ontology Deductive Modules
Authors: Hui Yang, Yue Ma, Nicole Bidoit
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on real-world ontologies show that our pseudominimal modules are indeed minimal modules in almost all cases (98.9%), and computing pseudo-minimal modules is more efficient (99.79 times faster on average) than the stateof-the-art method Zoom for computing minimal modules. Also, our complete modules are more compact than modules, but their computation time remains comparable. |
| Researcher Affiliation | Academia | Hui Yang, Yue Ma, Nicole Bidoit LISN, CNRS, Universit e Paris-Saclay yang@lisn.fr, ma@lisn.fr, nicole.bidoit@lisn.fr |
| Pseudocode | No | The paper describes the algorithm using text and equations but does not provide a formal pseudocode block. |
| Open Source Code | Yes | We implemented a prototype For Mod3 of our algorithm in Python and evaluated it over three real-world ontologies: https://gitlab.lisn.upsaclay.fr/yang/formod |
| Open Datasets | Yes | We implemented a prototype For Mod3 of our algorithm in Python and evaluated it over three real-world ontologies: Snomed CT (versions Jan 2016 and Jan 2021) and NCI (version 16.03d)4. We denote them as sn16, sn21, nci, respectively. Here, sn16 and nci are two EL-terminologies containing 317891 and 165341 axioms respectively. And sn21 is an EL-ontology with 362638 axioms. |
| Dataset Splits | No | The paper does not explicitly detail training, validation, or test splits. It mentions running experiments over '2 sets ΣO n of 1000 randomly generated signatures', but not data splits for model training/validation/testing. |
| Hardware Specification | Yes | All the experiments run on a machine with an Intel Xeon Core 4 Duo CPU 2.50 GHz with 64 Gi B of RAM. |
| Software Dependencies | No | The paper mentions the prototype For Mod is implemented in Python but does not provide specific version numbers for Python or any other libraries/dependencies. |
| Experiment Setup | No | The paper states: "For each ontology O, we run the experiments over 2 sets ΣO n of 1000 randomly generated signatures, where each signature has n concepts (n {50, 100}) and 10 roles." It also mentions a "total run-time limit of 600s". However, it does not provide details on hyperparameters or specific model configuration settings beyond this. |