AAAI Publications, Thirteenth International Conference on the Principles of Knowledge Representation and Reasoning

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Towards Parallel Nonmonotonic Reasoning with Billions of Facts
Ilias Tachmazidis, Grigoris Antoniou, Giorgos Flouris, Spyros Kotoulas

Last modified: 2012-05-17


We are 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 domain-specific 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 nonmonotonic 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.


Defeasible Reasoning; Parallel Computing; High Performance Computing; Parallel Reasoning

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