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MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Parallel Inferencing for OWL Knowledge Bases
ICPP '08 Proceedings of the 2008 37th International Conference on Parallel Processing
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ASWC '08 Proceedings of the 3rd Asian Semantic Web Conference on The Semantic Web
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ESCIENCE '08 Proceedings of the 2008 Fourth IEEE International Conference on eScience
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Web Semantics: Science, Services and Agents on the World Wide Web
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ISWC '09 Proceedings of the 8th International Semantic Web Conference
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CloudCom '09 Proceedings of the 1st International Conference on Cloud Computing
Web Semantics: Science, Services and Agents on the World Wide Web
LUBM: A benchmark for OWL knowledge base systems
Web Semantics: Science, Services and Agents on the World Wide Web
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SAOR: template rule optimisations for distributed reasoning over 1 billion linked data triples
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
Optimizing enterprise-scale OWL 2 RL reasoning in a relational database system
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
Signal/collect: graph algorithms for the (semantic) web
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I
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ESWC'10 Proceedings of the 7th international conference on The Semantic Web: research and Applications - Volume Part I
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ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
Checking and handling inconsistency of DBpedia
WISM'12 Proceedings of the 2012 international conference on Web Information Systems and Mining
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Advanced Engineering Informatics
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Future Generation Computer Systems
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The large amount of Semantic Web data and its fast growth pose a significant computational challenge in performing efficient and scalable reasoning. On a large scale, the resources of single machines are no longer sufficient and we are required to distribute the process to improve performance. In this article, we propose a distributed technique to perform materialization under the RDFS and OWL ter Horst semantics using the MapReduce programming model. We will show that a straightforward implementation is not efficient and does not scale. Our technique addresses the challenge of distributed reasoning through a set of algorithms which, combined, significantly increase performance. We have implemented WebPIE (Web-scale Inference Engine) and we demonstrate its performance on a cluster of up to 64 nodes. We have evaluated our system using very large real-world datasets (Bio2RDF, LLD, LDSR) and the LUBM synthetic benchmark, scaling up to 100 billion triples. Results show that our implementation scales linearly and vastly outperforms current systems in terms of maximum data size and inference speed.