Making Cross Products and Guarded Ontology Languages Compatible

Authors: Pierre Bourhis, Michael Morak, Andreas Pieris

IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical Our goal is to give an answer to the above question. To this end, we focus on the guarded fragment of first-order logic (which serves as a unifying framework that subsumes many of the aforementioned ontology languages) extended with cross products, and we investigate the standard tasks of satisfiability and query answering. Interestingly, we isolate relevant fragments that are compatible with cross products. ... After extending the guarded fragment with cross products, we focus on satisfiability. The decidability of the problem depends on the arity of the cross products... We show that the problem is undecidable, even for ternary cross products, but 2EXPTIME-complete for binary cross products.
Researcher Affiliation Academia 1CRIStAL, CNRS & Universit e Lille 1, France 2Institute of Information Systems, TU Wien, Austria 3School of Informatics, University of Edinburgh, UK
Pseudocode No The paper does not contain any pseudocode or clearly labeled algorithm blocks.
Open Source Code No The paper does not provide any statement or link indicating the availability of open-source code for the described methodology.
Open Datasets No The paper is theoretical and does not involve empirical experiments with datasets, training, or data access.
Dataset Splits No The paper is theoretical and does not involve empirical experiments with datasets or validation splits.
Hardware Specification No The paper focuses on theoretical analysis of logical frameworks and does not mention any specific hardware used for computational experiments.
Software Dependencies No The paper describes theoretical work and does not specify any software dependencies with version numbers for reproducing experiments.
Experiment Setup No The paper discusses theoretical results and does not describe an experimental setup with hyperparameters or system-level training settings.