This paper is concerned with the efficient computation of materialization in a knowledge base with a large ABox. We present a framework for performing this task on a shared-nothing parallel machine. The framework partitions TBox and ABox axioms using a min-min strategy. It utilizes an existing system, like SwiftOWLIM, to perform local inference computations and coordinates exchange of relevant information between processors. Our approach is able to exploit parallelism in the axioms of the TBox to achieve speedup in a cluster. However, this approach is limited by the complexity of the TBox. We present an experimental evaluation of the framework using datasets from the Lehigh University Benchmark (LUBM).
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机译：本文涉及在具有大型ABox的知识库中对实现的有效计算。我们提出了一个在无共享并行机上执行此任务的框架。该框架使用最小-最小策略对TBox和ABox公理进行分区。它利用现有系统（如SwiftOWLIM）执行本地推理计算并协调处理器之间相关信息的交换。我们的方法能够利用TBox公理中的并行性来实现集群中的加速。但是，这种方法受到TBox复杂性的限制。我们使用Lehigh University Benchmark（LUBM）的数据集对框架进行了实验评估。
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