SPARQL 1.1 offers a type of navigational query for RDF systems, called regular path query (RPQ). A regular path query allows for retrieving node pairs with the paths between them satisfying regular expressions. Regular path queries are always difficult to be evaluated efficiently because of the possible large search space. Thus there has been no scalable and practical solution so far. In this paper, we present Leon+, an in-memory distributed framework, to address the RPQ problem in the context of the knowledge graph. To reduce search space and mitigate mounting communication costs, Leon+ takes advantage of join-ahead pruning via a novel RDF summarization technique together with a path partitioning strategy. We also develop a subtle cost model to devise query plans to achieve high efficiency for complex RPQs. As there has been no available RPQ benchmark, we create micro-benchmarks on both synthetic and real-world datasets. A thorough experimental evaluation is presented between our approach and the state-of-the-art RDF stores. The results show that our approach outperforms 5x faster than the competitors on single RPQ. For query workload, it saves up to 1/2 time and 2/3 communication overheads over the baseline method.

SPARQL 1.1为RDF系统提供了一种导航查询，称为常规路径查询（RPQ）。常规路径查询允许检索节点对以及它们之间满足常规表达式的路径。由于可能存在较大的搜索空间，因此通常很难高效地评估常规路径查询。因此，到目前为止，还没有可扩展且实用的解决方案。在本文中，我们提出了内存分布式框架Leon +，以在知识图的背景下解决RPQ问题。为了减少搜索空间并降低通信成本，Leon +通过新颖的RDF汇总技术和路径分区策略利用了预先加入修剪的优势。我们还开发了一个微妙的成本模型来设计查询计划，以实现复杂RPQ的高效率。由于没有可用的RPQ基准，因此我们在合成数据集和实际数据集上创建微基准。我们的方法和最新的RDF存储之间进行了全面的实验评估。结果表明，我们的方法的性能比单个RPQ的竞争对手快5倍。对于查询工作负载，与基准方法相比，它最多可以节省1/2的时间和2/3的通信开销。