Rethinking Defeasible Reasoning: A Scalable Approach

TL;DR

This paper introduces a new scalable defeasible reasoning logic capable of handling billions of facts, addressing the limitations of traditional approaches in large data environments.

Contribution

A novel defeasible reasoning logic designed for scalability, with proven properties and demonstrated effectiveness on large-scale data sets.

Findings

Scalable reasoning applied to billions of facts.

The new logic maintains key properties of existing defeasible logics.

Experimental results confirm improved scalability and efficiency.

Abstract

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.