Parallel Inferencing for OWL Knowledge Bases

We examine the problem of parallelizing the inferencing process for OWL knowledge-bases. A key challenge in this problem is partitioning the computational workload of this process to minimize duplication of computation and the amount of data communicated among processors. We investigate two approaches to address this challenge. In the data partitioning approach, the data-set is partitioned into smaller units, which are then processed independently. In the rule partitioning approach the rule-base is partitioned and the smaller rule-bases are applied to the complete data set. We present various algorithms for the partitioning and analyze their advantages and disadvantages. A parallel inferencing algorithm is presented which uses the partitions that are created by the two approaches. We then present an implementation based on a popular open source OWL reasoner and on a networked cluster. Our experimental results show significant speedups for some popular benchmarks, thus making this a promising approach.