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QueryPIE: Backward reasoning for OWL Horst over very large knowledge bases (2011)
| Venue: | In Proc. 10th Int. Semantic Web Conf. (ISWC’11 |
| Citations: | 18 - 1 self |
BibTeX
@INPROCEEDINGS{Urbani11querypie:backward,
author = {Jacopo Urbani and Frank Van Harmelen and Stefan Schlobach and Henri Bal},
title = {QueryPIE: Backward reasoning for OWL Horst over very large knowledge bases},
booktitle = {In Proc. 10th Int. Semantic Web Conf. (ISWC’11},
year = {2011},
publisher = {Springer}
}
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Abstract
Abstract. Both materialization and backward-chaining as different modes of performing inference have complementary advantages and disadvantages. Materialization enables very efficient responses at query time, but at the cost of an expensive up front closure computation, which needs to be redone every time the knowledge base changes. Backward-chaining does not need such an expensive and change-sensitive precomputation, and is therefore suitable for more frequently changing knowledge bases, but has to perform more computation at query time. Materialization has been studied extensively in the recent semantic web literature, and is now available in industrial-strength systems. In this work, we focus instead on backward-chaining, and we present an hybrid algorithm to perform efficient backward-chaining reasoning on very large datasets expressed in the OWL Horst (pD∗) fragment. As a proof of concept, we have implemented a prototype called QueryPIE (Query Parallel Inference Engine), and we have tested its performance on different datasets of up to 1 billion triples. Our parallel implementation greatly reduces the reasoning complexity of a naive backward-chaining approach and returns results for single query-patterns in the order of milliseconds when running on a modest 8 machine cluster. To the best of our knowledge, QueryPIE is the first reported backwardchaining reasoner for OWL Horst that efficiently scales to a billion triples. 1
Keyphrases
owl horst large knowledge base query time recent semantic web literature parallel implementation machine cluster industrial-strength system return result knowledge base single query-patterns backwardchaining reasoner query parallel inference engine change-sensitive precomputation large datasets hybrid algorithm naive backward-chaining approach efficient backward-chaining reasoning efficient response different datasets front closure computation knowledge base change complementary advantage different mode
