Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Efficient Extraction of EL-Ontology Deductive Modules
Authors: Hui Yang, Yue Ma, Nicole Bidoit
AAAI 2023 | Venue PDF | 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 EMAIL, EMAIL, EMAIL |
| 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. |