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An Agglomerative-adapted Partition Approach for Large-scale Graphs

机译：大规模图的聚集自适应分区方法

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In recent years, an increasing number of knowledge bases have been built using linked data, thus datasets have grown substantially. It is neither reasonable to store a large amount of triple data in a single graph, nor appropriate to store RDF in named graphs by class URIs, because many joins can cause performance problems between graphs. This paper presents an agglomerative-adapted approach for large-scale graphs, which is also a bottom-up merging process. The proposed algorithm can partition triples data in three levels: blank nodes, associated nodes, and inference nodes. Regarding blank nodes and classesodes involved in reasoning rules, it is better to store with an optimal neighbor node in the same partition instead of splitting into separate partitions. The process of merging associated nodes needs to start with the node in the smallest cost and then repeat it until the final number of partitions is met. Finally, the feasibility and rationality of the merging algorithm are analyzed in detail through bibliographic cases. In summary, the partitioning methods proposed in this paper can be applied in distributed storage, data retrieval, data export, and semantic reasoning of large-scale triples graphs. In the future, we will research the automation setting of the number of partitions with machine learning algorithms.
机译：近年来，使用链接数据建立了越来越多的知识库，因此数据集已经大大增加。在单个图中存储大量的三元数据既不合理，也不适合通过类URI在命名图中存储RDF，因为许多联接会导致图形之间的性能问题。本文提出了一种适用于大型图的凝聚方法，这也是一种自下而上的合并过程。所提出的算法可以将三元组数据划分为三个级别：空白节点，关联节点和推理节点。对于推理规则中涉及的空白节点和类/节点，最好将最佳邻居节点存储在同一分区中，而不是拆分为单独的分区。合并关联节点的过程需要从成本最低的节点开始，然后重复进行直到达到分区的最终数量。最后，通过书目案例详细分析了合并算法的可行性和合理性。综上所述，本文提出的分区方法可以应用于大规模三元图的分布式存储，数据检索，数据导出和语义推理。将来，我们将使用机器学习算法研究分区数量的自动化设置。

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