Basic Probabilistic Ontological Data Exchange with Existential Rules

Authors: Thomas Lukasiewicz, Maria Vanina Martinez, Livia Predoiu, Gerardo I. Simari

AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We study the complexity of exchanging probabilistic data between ontology-based probabilistic databases. We provide an extensive complexity analysis of the problem of deciding the existence of a probabilistic (universal) solution for a given probabilistic source database relative to a (probabilistic) data exchange problem for the different languages considered.
Researcher Affiliation Academia 1Department of Computer Science, University of Oxford, UK 2Institute for Computer Science and Engineering (Universidad Nacional del Sur CONICET), Bahia Blanca, Argentina 3Department of Computer Science, Otto-von-Guericke University, Magdeburg, Germany
Pseudocode No The paper does not contain any structured pseudocode or algorithm blocks. It discusses theoretical complexity classes and problem solvability without providing implementation details in pseudocode.
Open Source Code No This paper is theoretical and focuses on complexity analysis; it does not describe an implemented methodology for which open-source code would typically be provided. No statement about code release for the work described in this paper was found.
Open Datasets No This paper is theoretical and does not involve empirical training on datasets. Example data is provided for illustrative purposes (Table 1), not as a dataset for experimentation.
Dataset Splits No No empirical experiments are conducted, therefore no training/validation/test dataset splits are discussed.
Hardware Specification No This is a theoretical paper focused on computational complexity; it does not describe any experiments that would require specific hardware. No hardware specifications are mentioned.
Software Dependencies No This is a theoretical paper and does not describe software implementations or dependencies with version numbers.
Experiment Setup No No empirical experiments are conducted, therefore no experimental setup details like hyperparameters or training configurations are provided.