RORS: Enhanced Rule-based OWL Reasoning on Spark

TL;DR

This paper introduces RORS, a Spark-based system that optimizes rule execution order for OWL reasoning, achieving about 30% faster performance by classifying rules and selecting an optimal execution sequence.

Contribution

It proposes a locally optimal rule execution strategy for OWL reasoning on Spark, improving performance by 30% over previous algorithms.

Findings

RORS reduces reasoning time by approximately 30%.

Class-based rule grouping enhances reasoning efficiency.

Experimental validation on LUBM dataset demonstrates effectiveness.

Abstract

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), sameAs rules (7 rules), and schema rules (8 rules) since, as we investigated, those triples corresponding to the first three classes of rules are overwhelming (e.g., over 99% in the LUBM dataset) in our practical world. Secondly, based on the interdependence among those entailment rules in each class, we pick out an optimal rule executable order of each class and then combine them into a new rule execution order of all rules. Finally, we implement the new rule execution order on Spark in a prototype called RORS. The experimental results show that the running time of RORS is improved by about 30% as compared to Kim & Park's algorithm (2015) using the LUBM200 (27.6 million triples).