Efficient Computation of the Well-Founded Semantics over Big Data
This addresses the challenge of scaling complex logic programming to big data for researchers and practitioners in AI and data science, though it is incremental as it adapts existing semantics to a new framework.
The paper tackles the problem of applying the well-founded semantics, a form of nonmonotonic reasoning in logic programming, to big data by proposing a parallel approach using MapReduce, achieving scalability to billions of facts.
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).