Ontology-Mediated Querying with the Description Logic EL: Trichotomy and Linear Datalog Rewritability

Authors: Carsten Lutz, Leif Sabellek

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

Reproducibility Variable Result LLM Response
Research Type Theoretical We consider ontology-mediated queries (OMQs) based on an EL ontology and an atomic query (AQ), provide an ultimately fine-grained analysis of data complexity and study rewritability into linear Datalog aiming to capture linear recursion in SQL. Our main results are that every such OMQ is in AC0, NL-complete or PTIME-complete, and that containment in NL coincides with rewritability into linear Datalog (whereas containment in AC0 coincides with rewritability into first-order logic). We establish natural characterizations of the three cases, show that deciding linear Datalog rewritability (as well as the mentioned complexities) is EXPTIMEcomplete, give a way to construct linear Datalog rewritings when they exist, and prove that there is no constant bound on the arity of IDB relations in linear Datalog rewritings.
Researcher Affiliation Academia Carsten Lutz and Leif Sabellek University of Bremen, Germany {clu,sabellek}@informatik.uni-bremen.de
Pseudocode Yes The program ΠQ,k consists of five types of rules: Start rules: Pcl T (S)(x) S(x) for all S N and where S(x) abbreviates V A S A(x); Extension rules: Pt1,...,tm,cl T (S)(x1, . . . , xm, y) Pt1,...,tm(x1, . . . , xm) S(y) for all S N and T -types t1, . . . , tm; Step rules: Pt1,...,tm 1,t(x1, . . . , xm 1, y) Pt1,...,tm(x1, . . . , xm) r(y, xm) S(y) for all S N and T -types t1, . . . , tm where t = cl T (S { r.A | A tm}); Consolidation rules: Pt1,...,tm 2,t(x1, . . . , xm 1) Pt1,...,tm(x1, . . . , xm 1, xm 1) for all S N and T -types t1, . . . , tm, t where t = cl T (tm 1 tm); Goal rules: goal(x) Pt(x) for all T -types t with A0 t.
Open Source Code No Proof details are in the appendix, which is provided at http://www.cs.uni-bremen.de/tdki/research/papers.html. This link is for supplementary materials (proofs) and does not explicitly state that the source code for the methodology is available.
Open Datasets No The paper is theoretical and does not use or refer to publicly available datasets for training or evaluation in the context of empirical studies.
Dataset Splits No The paper is theoretical and does not describe empirical experiments with training, validation, or test dataset splits.
Hardware Specification No The paper is theoretical and does not mention any specific hardware used for its research or computations.
Software Dependencies No The paper is theoretical and does not mention any specific software dependencies with version numbers.
Experiment Setup No The paper is theoretical and does not describe any specific experimental setup details such as hyperparameters or training configurations.