Abstract
With the rapid development of semantic Web and big data technology, ontology data has the characteristics of large scale, high speed growth and diversity which big data has. On one hand, the conventional ontology reasoners do not scale well for large amounts of ontologies because they are designed for run on a single machine. On the other hand, the existing scalable reasoners are not perfect enough, for example, to completely support the widely used Semantic Web Rule Language (SWRL) rules. This paper presents a method for SWRL scalable parallel reasoning using the Spark SQL programming model. Our method supports SWRL rules more completely, and because of the use of Spark SQL, our algorithm is simpler and can rely on Spark SQL's built-in optimizations to provide optimized execution efficiency. We also considered some optimizations to speed up the rule reasoning process.
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