Stratified Negation in Limit Datalog Programs

Authors: Mark Kaminski, Bernardo Cuenca Grau, Egor V. Kostylev, Boris Motik, Ian Horrocks

IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical In this paper, we study an extension of limit programs with stratified negation-as-failure. We show that the additional expressive power makes reasoning computationally more demanding, and provide tight data complexity bounds. We also identify a fragment with tractable data complexity and sufficient expressivity to capture many relevant tasks.
Researcher Affiliation Academia Mark Kaminski, Bernardo Cuenca Grau, Egor V. Kostylev, Boris Motik and Ian Horrocks Department of Computer Science, University of Oxford, UK
Pseudocode Yes ALGORITHM 1: Parameter: oracle O computing P(P ) for P a semi-positive, limit-linear program Input: stratified, limit-linear program P, fact α Output: true if P |= α
Open Source Code No The proofs of all our results are given in an extended version of this paper available online at ar Xiv:1804.09473.
Open Datasets No The paper is theoretical and does not describe experiments that would use a dataset for training or evaluation. It refers to 'datasets' Dsp and Dcc for illustrative examples, but provides no access information or details about their public availability.
Dataset Splits No The paper is theoretical and does not include empirical experiments with dataset splits for training, validation, or testing.
Hardware Specification No The paper is theoretical and does not report on experiments or specify any hardware used.
Software Dependencies No The paper focuses on theoretical aspects of logic programming and Datalog, and does not mention specific software dependencies with version numbers.
Experiment Setup No The paper is theoretical and does not report on experiments, thus no experimental setup details like hyperparameters are provided.