Scalable OWL-Horst ontology reasoning using SPARK

In this paper, we present an approach to perform reasoning for scalable OWL ontologies in a Hadoop-based distributed computing cluster. Rule-based reasoning is typically used for a scalable OWL-Horst reasoning; typically, the system repeatedly performs many operations involving semantic axioms for big ontology triples until no further inferred data exists. Thus, the reasoning systems suffer from performance limitations when ontology reasoning is performed via disk-based MapReduce approaches. To overcome this drawback, we propose an approach that loads triples to memory in computer nodes that are connected by SPARK - a memory-based cluster computing platform - and executes ontology reasoning. To implement an OWL Horst ontology reasoning system, we first define a set of algorithms such that they divide large triples into Resilient Distributed Datasets (RDDs), taking into account the patterns and interdependencies of the reasoning rules. We then load each RDD into the memory of computers composing a distributed computing cluster and subsequently perform distributed reasoning by rule execution orders. To evaluate the proposed methods, we compare it to WebPIE using the LUBM set, which is formal dataset for evaluating ontology inferences and search speeds. The proposed approach shows throughput is improved by 200% (98k/sec) as compared to WebPIE (33k/sec) using the LUBM6000 (860 million triples, 109 gigabyte).