Toward a More Efficient Generation of Structured Argumentation Graphs

Bruno Yun 1 Madalina Croitoru 1
1 GRAPHIK - Graphs for Inferences on Knowledge
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : To address the needs of the EU NoAW project, in this paper we solve the problem of efficiently generating the argumentation graphs from knowledge bases expressed using existential rules. For the knowledge bases without rules, we provide a methodology that allows to optimise the generation of argumentation graphs. For knowledge bases with rules, we show how to filter out a large number of arguments and reduce the number of attacks.
Type de document :
Communication dans un congrès
COMMA: Conference on Computational Models of Argument, Sep 2018, Varsovie, Poland. 7th International Conference on Computational Models of Argument, 305, pp.205-212, 2018, Frontiers in Artificial Intelligence and Applications. 〈http://comma2018.argdiap.pl/〉. 〈10.3233/978-1-61499-906-5-205〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01892707
Contributeur : Bruno Yun <>
Soumis le : mercredi 10 octobre 2018 - 18:24:42
Dernière modification le : mercredi 13 février 2019 - 16:32:01

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Bruno Yun, Madalina Croitoru. Toward a More Efficient Generation of Structured Argumentation Graphs. COMMA: Conference on Computational Models of Argument, Sep 2018, Varsovie, Poland. 7th International Conference on Computational Models of Argument, 305, pp.205-212, 2018, Frontiers in Artificial Intelligence and Applications. 〈http://comma2018.argdiap.pl/〉. 〈10.3233/978-1-61499-906-5-205〉. 〈lirmm-01892707〉

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