Mixed-World Reasoning with Existential Rules under Active-Domain Semantics
Authors: Meghyn Bienvenu, Pierre Bourhis
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we study reasoning with existential rules in a setting where some of the predicates may be closed... We show, unsurprisingly, that the main reasoning tasks (satisfiability and certainty / possibility of Boolean queries) are all intractable in data complexity in the general case. However, several positive (PTIME data) results are obtained for the linear fragment, and interestingly, these tractability results hold also for various extensions... |
| Researcher Affiliation | Academia | Meghyn Bienvenu1 , Pierre Bourhis2 1CNRS, La BRI, University of Bordeaux and Bordeaux INP, Talence, France 2CNRS, CRISt AL, University of Lille, Centrale Lille and INRIA Lille, Lille, France |
| Pseudocode | No | The paper includes formal definitions, theorems, and proof sketches, but it does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that open-source code for the described methodology is available. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments with datasets, thus it does not mention public dataset availability for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical data, therefore it does not discuss training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not involve computational experiments that would require specific hardware, therefore no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and focuses on logical formalisms and complexity analysis, thus it does not list software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, therefore it does not include details about an experimental setup, such as hyperparameters or training settings. |