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Towards Parallel Nonmonotonic Reasoning with Billions of Facts

Tachmazidis, Ilias, Antoniou, Grigoris, Flouris, Giorgos and Kotoulas, Spyros (2012) Towards Parallel Nonmonotonic Reasoning with Billions of Facts. In: Principles of Knowledge Representation and Reasoning: Proceedings. KR 2012 . AAAI Press, Rome, Italy, pp. 638-642.

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    We are recently witnessing an explosion of available
    data from the Web, government authorities, scientific
    databases, sensors and more. Such datasets could benefit
    from the introduction of rule sets encoding commonly
    accepted rules or facts, application- or domainspecific
    rules, commonsense knowledge etc. This raises
    the question of whether, how, and to what extent knowledge
    representation methods are capable of handling the
    vast amounts of data for these applications. In this paper,
    we consider non-monotonic reasoning, which has
    traditionally focused on rich knowledge structures. In
    particular, we consider defeasible logic, and analyze
    how parallelization, using the MapReduce framework,
    can be used to reason with defeasible rules over huge
    data sets. Our experimental results demonstrate that defeasible
    reasoning with billions of data is performant,
    and has the potential to scale to trillions of facts.

    Item Type: Book Chapter
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Schools: School of Computing and Engineering
    Related URLs:
    Depositing User: Grigoris Antoniou
    Date Deposited: 26 Jul 2012 16:28
    Last Modified: 26 Jul 2012 16:28


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