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..
Learning Conceptual Space Representations of Interrelated Concepts
Authors: Zied Bouraoui, Steven Schockaert
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we experimentally evaluate our method2 against a number of baseline methods. |
| Researcher Affiliation | Academia | Zied Bouraoui CRIL CNRS & Univ Artois, France EMAIL Steven Schockaert Cardiff University, UK Schockaert EMAIL |
| Pseudocode | No | The paper describes the proposed methods using text and mathematical equations, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Implementation and data available at https://github.com/flexilogalgo |
| Open Datasets | Yes | In our experiments, we have used SUMO3, which is a large open-domain ontology. ... SUMO3, http://www.adampease.org/OP/. Babel Net4, Babel Net Java API available at http://babelnet.org |
| Dataset Splits | Yes | We split the set of individuals into a training set Itrain containing 2/3 of all individuals, and a test set Itest containing the remaining 1/3. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used for the experiments, such as CPU or GPU models. |
| Software Dependencies | No | The paper mentions using Babel Net Java API, but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | The number of Gibbs sample that we used is equal to 1000 where each sample is generated after 25 every 25 iterations. The burn-in period that we use is fixed to 200 samples. |