Recent technological advances have led to unprecedented amounts of generated data that originate from the Web, sensor networks, and social media. Analytics in terms of defeasible reasoning – for example, for decision making – could provide richer knowledge of the underlying domain. Traditionally, defeasible reasoning has focused on complex knowledge structures over small to medium amounts of data, but recent research efforts have attempted to parallelize the reasoning process over theories with large numbers of facts. Such work has shown that traditional defeasible logics come with overheads that limit scalability. In this work, we design a new logic for defeasible reasoning, thus ensuring scalability by design. We establish several properties of the logic, including its relation to existing defeasible logics. Our experimental results indicate that our approach is indeed scalable and defeasible reasoning can be applied to billions of facts.

中文翻译：

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重新思考可废止推理：一种可扩展的方法

最近的技术进步已经产生了前所未有的大量来自 Web、传感器网络和社交媒体的生成数据。可废止推理方面的分析（例如，用于决策制定）可以提供更丰富的基础领域知识。传统上，可废止推理侧重于中小数据量的复杂知识结构，但最近的研究努力试图将推理过程与具有大量事实的理论并行化。这样的工作表明，传统的可废止逻辑带有限制可扩展性的开销。在这项工作中，我们为可废止推理设计了一种新逻辑，从而通过设计确保了可扩展性。我们建立了逻辑的几个属性，包括它与现有可废止逻辑的关系。