Efficient Computation of the Well-Founded Semantics over Big Data

TL;DR

This paper presents a scalable, parallel approach using MapReduce to compute well-founded semantics over billions of facts, enabling nonmonotonic reasoning on large-scale data without stratification restrictions.

Contribution

It introduces the first large-scale, nonmonotonic reasoning method for big data using parallel MapReduce, overcoming previous limitations of stratification.

Findings

Approach is scalable to billions of facts

Well-founded semantics can be applied to Big Data

First to address large-scale nonmonotonic reasoning without stratification

Abstract

Data originating from the Web, sensor readings and social media result in increasingly huge datasets. The so called Big Data comes with new scientific and technological challenges while creating new opportunities, hence the increasing interest in academia and industry. Traditionally, logic programming has focused on complex knowledge structures/programs, so the question arises whether and how it can work in the face of Big Data. In this paper, we examine how the well-founded semantics can process huge amounts of data through mass parallelization. More specifically, we propose and evaluate a parallel approach using the MapReduce framework. Our experimental results indicate that our approach is scalable and that well-founded semantics can be applied to billions of facts. To the best of our knowledge, this is the first work that addresses large scale nonmonotonic reasoning without the restriction of stratification for predicates of arbitrary arity. To appear in Theory and Practice of Logic Programming (TPLP).