Large-scale Parallel Stratified Defeasible Reasoning

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Citations (Scopus)

Abstract

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.

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
Pages738-743
Number of pages6
ISBN (Print)9781614990970
DOIs
Publication statusPublished - Jan 2012

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume242
ISSN (Print)0922-6389

Fingerprint

Knowledge representation
Explosions
Sensors

Cite this

McCluskey, L., Antoniou, G., Tachmazidis, I., Flouris, G., & Kotoulas, S. (2012). Large-scale Parallel Stratified Defeasible Reasoning. In ECAI 2012 - 20th European Conference on Artificial Intelligence, 27-31 August 2012, Montpellier, France - Including Prestigious Applications of Artificial Intelligence (PAIS-2012) System Demonstration (pp. 738-743). (Frontiers in Artificial Intelligence and Applications; Vol. 242). IOS Press. https://doi.org/10.3233/978-1-61499-098-7-738
McCluskey, Lee ; Antoniou, Grigoris ; Tachmazidis, Ilias ; Flouris, Giorgos ; Kotoulas, Spyros. / Large-scale Parallel Stratified Defeasible Reasoning. ECAI 2012 - 20th European Conference on Artificial Intelligence, 27-31 August 2012, Montpellier, France - Including Prestigious Applications of Artificial Intelligence (PAIS-2012) System Demonstration. IOS Press, 2012. pp. 738-743 (Frontiers in Artificial Intelligence and Applications).
@inproceedings{2d8d9b6ad92e430199a134b03e84da9c,
title = "Large-scale Parallel Stratified Defeasible Reasoning",
abstract = "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.",
author = "Lee McCluskey and Grigoris Antoniou and Ilias Tachmazidis and Giorgos Flouris and Spyros Kotoulas",
year = "2012",
month = "1",
doi = "10.3233/978-1-61499-098-7-738",
language = "English",
isbn = "9781614990970",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press",
pages = "738--743",
booktitle = "ECAI 2012 - 20th European Conference on Artificial Intelligence, 27-31 August 2012, Montpellier, France - Including Prestigious Applications of Artificial Intelligence (PAIS-2012) System Demonstration",
address = "United States",

}

McCluskey, L, Antoniou, G, Tachmazidis, I, Flouris, G & Kotoulas, S 2012, Large-scale Parallel Stratified Defeasible Reasoning. in ECAI 2012 - 20th European Conference on Artificial Intelligence, 27-31 August 2012, Montpellier, France - Including Prestigious Applications of Artificial Intelligence (PAIS-2012) System Demonstration. Frontiers in Artificial Intelligence and Applications, vol. 242, IOS Press, pp. 738-743. https://doi.org/10.3233/978-1-61499-098-7-738

Large-scale Parallel Stratified Defeasible Reasoning. / McCluskey, Lee; Antoniou, Grigoris; Tachmazidis, Ilias; Flouris, Giorgos; Kotoulas, Spyros.

ECAI 2012 - 20th European Conference on Artificial Intelligence, 27-31 August 2012, Montpellier, France - Including Prestigious Applications of Artificial Intelligence (PAIS-2012) System Demonstration. IOS Press, 2012. p. 738-743 (Frontiers in Artificial Intelligence and Applications; Vol. 242).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Large-scale Parallel Stratified Defeasible Reasoning

AU - McCluskey, Lee

AU - Antoniou, Grigoris

AU - Tachmazidis, Ilias

AU - Flouris, Giorgos

AU - Kotoulas, Spyros

PY - 2012/1

Y1 - 2012/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84878790110&partnerID=8YFLogxK

U2 - 10.3233/978-1-61499-098-7-738

DO - 10.3233/978-1-61499-098-7-738

M3 - Conference contribution

SN - 9781614990970

T3 - Frontiers in Artificial Intelligence and Applications

SP - 738

EP - 743

BT - ECAI 2012 - 20th European Conference on Artificial Intelligence, 27-31 August 2012, Montpellier, France - Including Prestigious Applications of Artificial Intelligence (PAIS-2012) System Demonstration

PB - IOS Press

ER -

McCluskey L, Antoniou G, Tachmazidis I, Flouris G, Kotoulas S. Large-scale Parallel Stratified Defeasible Reasoning. In ECAI 2012 - 20th European Conference on Artificial Intelligence, 27-31 August 2012, Montpellier, France - Including Prestigious Applications of Artificial Intelligence (PAIS-2012) System Demonstration. IOS Press. 2012. p. 738-743. (Frontiers in Artificial Intelligence and Applications). https://doi.org/10.3233/978-1-61499-098-7-738