Query Answering in Ontologies under Preference Rankings

Authors: İsmail İlkan Ceylan, Thomas Lukasiewicz, Rafael Peñaloza, Oana Tifrea-Marciuska

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

Reproducibility Variable Result LLM Response
Research Type Theoretical We provide a host of complexity results for the main computational tasks in this framework, for the general case, and for EL and DL-Litecore as underlying ontology languages. We also give generic complexity results for other important reasoning problems, namely, for deciding k most preferred conditional answers, for deciding a lower bound for the preference degree of a Boolean CQ (BCQ), and for deciding k most preferred worlds. Moreover, we give complexity results for these problems for EL and DL-Litecore, which include further tractability and first-order rewritability results.
Researcher Affiliation Academia Ismail Ilkan Ceylan1, Thomas Lukasiewicz2, Rafael Pe naloza3, Oana Tifrea-Marciuska4 1Theoretical Computer Science, Technische Universit at Dresden, Germany 2Department of Computer Science, University of Oxford, UK 3KRDB Research Centre, Free University of Bozen-Bolzano, Italy 4The Alan Turing Institute, London, UK
Pseudocode No The paper does not contain any structured pseudocode or algorithm blocks.
Open Source Code No The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available.
Open Datasets No The paper focuses on theoretical contributions, defining a framework and analyzing its complexity. It does not conduct empirical studies that would involve training on datasets.
Dataset Splits No The paper is theoretical and does not describe experimental validation on datasets, thus no dataset split information for validation is provided.
Hardware Specification No The paper does not describe any specific hardware used to run experiments, as it is a theoretical paper.
Software Dependencies No The paper is theoretical and does not describe software dependencies with specific version numbers for experimental reproducibility.
Experiment Setup No The paper is theoretical and does not include details about an experimental setup, such as hyperparameters or training configurations.