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..
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. |