Inconsistency-Tolerant Ontology-Based Data Access Revisited: Taking Mappings into Account
Authors: Meghyn Bienvenu
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | After formalizing the problem, we perform a detailed analysis of the data complexity of inconsistency-tolerant OBDA for ontologies formulated in DL-Lite and other data-tractable description logics, considering three different semantics (AR, IAR, and brave), two notions of repairs (subset and symmetric difference), and two classes of global-as-view (GAV) mappings. We show that adding plain GAV mappings does not affect data complexity, but there is a jump in complexity if mappings with negated atoms are considered. |
| Researcher Affiliation | Academia | Meghyn Bienvenu French National Center for Scientiļ¬c Research (CNRS) University of Montpellier French Institute for Research in Computer Science and Automation (Inria) |
| Pseudocode | No | The paper describes theoretical concepts, definitions, and theorems, but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper discusses existing OBDA systems and mapping languages (e.g., Ontop, R2RML) but does not provide a statement or link for open-sourcing the code for the work presented in this paper. |
| Open Datasets | No | The paper is a theoretical work focusing on complexity analysis and formal definitions. It does not use or refer to specific datasets for training or evaluation. |
| Dataset Splits | No | The paper is a theoretical work and does not involve empirical validation with dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is a theoretical study and does not include details about an experimental setup, hyperparameters, or training configurations. |