Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
A Description Logic for Analogical Reasoning
Authors: Steven Schockaert, Yazmin Ibanez-Garcia, Victor Gutierrez-Basulto
IJCAI 2021 | Venue PDF | LLM Run Details | Input Tokens: 16,315 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,722 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We start from the semantics proposed by Ib a nez-Garc ıa et al. [2020], which extends the EL semantics by assigning to each individual a set of features... We show that having access to these features in the semantics also allows us to formalise analogies. We then analyse the properties of analogies under the proposed semantics, and show among others how it enables two plausible inference patterns: rule translation and rule extrapolation. |
| Researcher Affiliation | Academia | Steven Schockaert , Yazm ın Ib a nez-Garc ıa , V ıctor Guti errez-Basulto Cardiff University, UK EMAIL |
| Pseudocode | No | The paper contains formal definitions and logical expressions but no pseudocode or algorithm blocks. |
| Open Source Code | No | An appendix with proofs of all results, as well as counterexamples for some claims, is available at https://arxiv.org/abs/2105.04620 - This link is for proofs, not source code. The paper is theoretical and does not mention any source code release. |
| Open Datasets | No | The paper is theoretical and does not use any datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe any experiments or dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not mention any hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies or versions. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup or hyperparameters. |