Terminological reasoning is inherently intractable (research note)
Artificial Intelligence
The well-founded semantics for general logic programs
Journal of the ACM (JACM)
Towards LarKC: A Platform for Web-Scale Reasoning
ICSC '08 Proceedings of the 2008 IEEE International Conference on Semantic Computing
OpenRuleBench: an analysis of the performance of rule engines
Proceedings of the 18th international conference on World wide web
Complexity of Subsumption in the EL Family of Description Logics: Acyclic and Cyclic TBoxes
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
A Formal Approach for RDF/S Ontology Evolution
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Marvin: Distributed reasoning over large-scale Semantic Web data
Web Semantics: Science, Services and Agents on the World Wide Web
Scalable Distributed Reasoning Using MapReduce
ISWC '09 Proceedings of the 8th International Semantic Web Conference
Optimizing joins in a map-reduce environment
Proceedings of the 13th International Conference on Extending Database Technology
Mind the data skew: distributed inferencing by speeddating in elastic regions
Proceedings of the 19th international conference on World wide web
High-performance computing applied to semantic databases
ESWC'11 Proceedings of the 8th extended semantic web conference on The semanic web: research and applications - Volume Part II
Containment and minimization of RDF/S query patterns
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
OWL reasoning with WebPIE: calculating the closure of 100 billion triples
ESWC'10 Proceedings of the 7th international conference on The Semantic Web: research and Applications - Volume Part I
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Increasingly huge amounts of data are published on the Web, and generated from sensors and social media. This Big Data challenge poses new scientific and technological challenges and creates new opportunities - thus the increasing attention 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 stratified semantics of logic programming, equivalent to the well-founded semantics for stratified programs, can process huge amounts of data through mass parallelization. In particular, we propose and evaluate a parallel approach using the MapReduce framework. Our experimental results indicate that our approach is scalable and that stratified semantics of logic programming can be applied to billions of facts.