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 [1].
Classifying Inconsistency Measures Using Graphs
Authors: Glauber De Bona, John Grant , Anthony Hunter, Sebastien Konieczny
JAIR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | To address these problems, we introduce a general framework for comparing syntactic measures of inconsistency. It is based on the notion of an inconsistency graph for each knowledgebase (a bipartite graph with a set of vertices representing formulas in the knowledgebase, a set of vertices representing minimal inconsistent subsets of the knowledgebase, and edges representing that a formula belongs to a minimal inconsistent subset). We then show that various measures can be computed using the inconsistency graph. Then we introduce abstractions of the inconsistency graph and use them to construct a hierarchy of syntactic inconsistency measures. Furthermore, we extend the inconsistency graph concept with a labeling that extends the hierarchy to include some other types of inconsistency measures. |
| Researcher Affiliation | Academia | Glauber De Bona EMAIL Escola Polit ecnica, Universidade de S ao Paulo, S ao Paulo, Brasil; John Grant EMAIL Department of Computer Science, and UMIACS, University of Maryland, College Park, MD, USA; Anthony Hunter EMAIL Department of Computer Science, University College London, London, UK; S ebastien Konieczny EMAIL CRIL, CNRS Universit e d Artois, Lens, France |
| Pseudocode | No | The paper focuses on introducing a theoretical framework, definitions, theorems, and proofs related to inconsistency measures and graphs. It does not present any algorithm or pseudocode blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code, nor does it provide links to any code repositories or supplementary materials containing code for the described methodology. |
| Open Datasets | No | The paper describes a theoretical framework for classifying inconsistency measures. It does not conduct experiments on specific datasets or provide access information for any publicly available or open datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets; therefore, there is no mention of training/test/validation dataset splits. |
| Hardware Specification | No | The paper describes theoretical contributions related to classifying inconsistency measures. It does not report on any experimental work that would require specific hardware, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper introduces a theoretical framework and mathematical concepts. It does not mention any specific software packages, libraries, or versions that would be necessary to replicate experimental results. |
| Experiment Setup | No | The paper is theoretical and focuses on developing a classification framework for inconsistency measures. It does not describe any empirical experiments, and therefore, no experimental setup details, hyperparameters, or training configurations are provided. |