Large-scale Parallel Stratified Defeasible Reasoning

Lee McCluskey, Grigoris Antoniou, Ilias Tachmazidis, Giorgos Flouris, Spyros Kotoulas

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

22 Citations (Scopus)


We are recently experiencing an unprecedented explosion of available data coming from the Web, sensors readings, scientific databases, government authorities and more. Such datasets could benefit from the introduction of rule sets encoding commonly accepted rules or facts, application- or domain-specific rules, commonsense knowledge etc. This raises the question of whether, how, and to what extent knowledge representation methods are capable of handling huge amounts of data for these applications. In this paper, we consider inconsistency-tolerant reasoning in the form of defeasible logic, and analyze how parallelization, using the MapReduce framework, can be used to reason with defeasible rules over huge datasets. We extend previous work by dealing with predicates of arbitrary arity, under the assumption of stratification. Moving from unary to multi-arity predicates is a decisive step towards practical applications, e.g. reasoning with linked open (RDF) data. Our experimental results demonstrate that defeasible reasoning with millions of data is performant, and has the potential to scale to billions of facts.

Original languageEnglish
Title of host publicationECAI 2012 - 20th European Conference on Artificial Intelligence, 27-31 August 2012, Montpellier, France - Including Prestigious Applications of Artificial Intelligence (PAIS-2012) System Demonstration
PublisherIOS Press
Number of pages6
ISBN (Print)9781614990970
Publication statusPublished - Jan 2012

Publication series

NameFrontiers in Artificial Intelligence and Applications
ISSN (Print)0922-6389


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