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WebPIE: A Web-scale parallel inference engine using
BibTeX
@MISC{Urbania_webpie:a,
author = {Jacopo Urbania and Spyros Kotoulasa and Jason Maassena and Frank Van Harmelena and Henri Bala},
title = {WebPIE: A Web-scale parallel inference engine using},
year = {}
}
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Abstract
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 materializa-tion 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 dis-tributed reasoning through a set of algorithms which, combined, significantly increase performance. We have implemented WebPIE (Web-scale Inference En-gine) 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.
Keyphrases
web-scale parallel inference engine semantic web data mapreduce programming model maximum data size large scale significant computational challenge increase performance straightforward implementation fast growth owl ter horst semantics large real-world datasets scalable reasoning distributed technique dis-tributed reasoning outperforms current system inference speed web-scale inference en-gine single machine lubm synthetic benchmark large amount
