The Bag Semantics of Ontology-Based Data Access

Authors: Charalampos Nikolaou, Egor V. Kostylev, George Konstantinidis, Mark Kaminski, Bernardo Cuenca Grau, Ian Horrocks

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

Reproducibility Variable Result LLM Response
Research Type Theoretical We show that, in contrast to the set case, ontologies may not have a universal model (i.e., a single model over which all CQs can be correctly evaluated), and bag query answering becomes CONP-hard in data complexity even if we restrict ourselves to DL-Litebag core ontologies. 3. To regain tractability, we study the class of rooted CQs [Bienvenu et al., 2012]... We show that rooted CQs over DL-Litebag core ontologies not only admit a universal model and enjoy favourable computational properties, but also allow for rewritings that can be directly evaluated over the bag ABox of the ontology.
Researcher Affiliation Academia Charalampos Nikolaou, Egor V. Kostylev, George Konstantinidis, Mark Kaminski, Bernardo Cuenca Grau, and Ian Horrocks Department of Computer Science, University of Oxford, UK
Pseudocode No The paper includes formal definitions, theorems, and proofs but no structured pseudocode or algorithm blocks.
Open Source Code No The paper does not provide any information or links to open-source code for the methodology described.
Open Datasets No The paper is theoretical and does not involve empirical training on datasets. No information about publicly available datasets is provided.
Dataset Splits No The paper is theoretical and does not involve empirical validation on datasets. No information about dataset splits is provided.
Hardware Specification No The paper is theoretical and does not describe empirical experiments, thus no hardware specifications are mentioned.
Software Dependencies No The paper is theoretical and does not describe empirical experiments, thus no specific software dependencies with version numbers are mentioned.
Experiment Setup No The paper is theoretical and does not include details about an experimental setup involving hyperparameters or training configurations.