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.
Non-expansive Fuzzy ALC
Authors: Stefan Gebhart, Lutz Schröder, Paul Wild
IJCAI 2025 | Venue PDF | LLM Run Details | Input Tokens: 15,741 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,589 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 present an unlabelled tableau method for non-expansive fuzzy ALC, which allows reasoning over general TBoxes in EXPTIME like in two-valued ALC. Our main technical result on non-expansive fuzzy ALC is decidability of the main reasoning problems in the same complexity as for two-valued ALC; most notably, threshold satisfiability over general TBoxes is (only) EXPTIME-complete, in sharp contrast with the undecidability encountered for full ukasiewicz fuzzy ALC, and in spite of the fact that the semantics of general concept inclusions is pointwise inequality and thus corresponds to validity of ukasiewicz implication. We base this result on an unlabelled tableau calculus. |
| Researcher Affiliation | Academia | Stefan Gebhart , Lutz Schr oder and Paul Wild Friedrich-Alexander-Universit at Erlangen-N urnberg EMAIL |
| Pseudocode | Yes | Algorithm 1: checking satisfiability in non-expansive fuzzy ALC |
| Open Source Code | No | No explicit statement or link regarding open-source code for the methodology described in this paper was found. |
| Open Datasets | No | The paper focuses on theoretical aspects of fuzzy description logics, introducing a new logic and a tableau calculus, and does not mention the use or availability of any datasets. |
| Dataset Splits | No | As the paper is theoretical and does not describe experiments using datasets, there is no mention of dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments or specific hardware used for computations. |
| Software Dependencies | No | The paper describes a theoretical framework and an algorithm but does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on logic and algorithm design, thus it does not include details on experimental setup or hyperparameters. |