RORS: Enhanced Rule-Based OWL Reasoning on Spark

  title={RORS: Enhanced Rule-Based OWL Reasoning on Spark},
  author={Zhihui Liu and Zhiyong Feng and Xiaowang Zhang and Xin Wang and Guozheng Rao},
The rule-based OWL reasoning is to compute the deductive closure of an ontology by applying RDF/RDFS and OWL entailment rules. The performance of the rule-based OWL reasoning is often sensitive to the rule execution order. In this paper, we present an approach to enhancing the performance of the rule-based OWL reasoning on Spark based on a locally optimal executable strategy. Firstly, we divide all rules (27 in total) into four main classes, namely, SPO rules (5 rules), type rules (7 rules… Expand
Enhancing Rule-based OWL Reasoning on Spark
The experimental results show that the approach to enhancing the performance of the rule-based OWL reasoning on Spark based on a locally optimal executable strategy achieves better performance as compared to Kim & Park’s algorithm. Expand
SPOWL: Spark-based OWL 2 Reasoning Materialisation
  • Yu Liu, P. McBrien
  • Computer Science
  • BeyondMR@SIGMOD
  • 2017
SPOWL acts as a compiler, which maps axioms in the T-Box of an ontology to Spark programmes, which will be executed iteratively to compute and materialise a closure of reasoning results entailed by the ontology. Expand
PRSPR: An Adaptive Framework for Massive RDF Stream Reasoning
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ComR: a combined OWL reasoner for ontology classification
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Distributed Reasoning of RDF Data
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An Adaptive Framework for RDF Stream Reasoning
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Linked Data


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