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].
Inconsistency Handling in DatalogMTL
Authors: Meghyn Bienvenu, Camille Bourgaux, Atefe Khodadaditaghanaki
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we explore the issue of inconsistency handling in Datalog MTL, an extension of Datalog with metric temporal operators. Since facts are associated with time intervals, there are different manners to restore consistency when they contradict the rules, such as removing facts or modifying their time intervals. Our first contribution is the definition of relevant notions of conflicts (minimal explanations for inconsistency) and repairs (possible ways of restoring consistency) for this setting and the study of the properties of these notions and the associated inconsistency-tolerant semantics. Our second contribution is a data complexity analysis of the tasks of generating a single conflict / repair and query entailment under repair-based semantics. |
| Researcher Affiliation | Academia | 1Univ. Bordeaux, CNRS, Bordeaux INP, La BRI, UMR 5800, Talence, France 2DI ENS, ENS, CNRS, PSL University & Inria, Paris, France 3Paderborn University, Paderborn, Germany EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes methods and concepts through formal definitions, propositions, and examples but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions existing implemented reasoning systems for Datalog MTL by other researchers (e.g., [Kalayci et al., 2019; Wang et al., 2022; Wang et al., 2024; Bellomarini et al., 2022; Walega et al., 2023b; Ivliev et al., 2024]) but does not state that the authors are releasing their own code for the methodology described in this paper. It provides a link to an arXiv preprint for proofs, not code. |
| Open Datasets | No | The paper discusses concepts using a running example about a blood transfusion scenario (Example 1 and 2), which seems illustrative rather than based on a specific public dataset. There is no mention of a specific public dataset with access information (link, citation, repository). |
| Dataset Splits | No | The paper is theoretical, focusing on definitions, properties, and complexity analysis. It does not conduct experiments on datasets that would require train/test/validation splits. |
| Hardware Specification | No | The paper is theoretical and focuses on complexity analysis and properties of logical formalisms. It does not describe any experimental setup that would involve specific hardware. |
| Software Dependencies | No | The paper discusses logical formalisms and their theoretical properties. It does not describe an implementation of the proposed methods, and therefore, does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical, providing definitions, properties, and complexity analysis. It does not describe any experiments, and therefore, no experimental setup details like hyperparameters or training configurations are present. |