The Semantic Web - ISWC 2019 - 18th International Semantic Web Conference, Auckland, New Zealand, October 26-30, 2019, Proceedings, Part I
The Semantic Web - ISWC 2019 - 18th International Semantic Web Conference, Auckland, New Zealand, October 26-30, 2019, Proceedings, Part I
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语义网 - ISWC 2019 - 第 18 届国际语义网会议，新西兰奥克兰，2019 年 10 月 26-30 日，会议记录，第一部分
DOI：
10.1007/978-3-030-30793-6_2
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Ajileye T
中科院分区：
文献类型：
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作者：
Ajileye T
几个集中式RDF系统支持数据库推理，通过使用众所周知的算法预计算和存储所有逻辑上隐含的三元组。大型RDF数据集通常超过集中式RDF系统的容量，常见的解决方案是将数据集分布在无共享服务器的集群中。虽然已知许多分布式查询回答技术，但对任意数据记录规则的分布式半智能评估却知之甚少。事实上，大多数分布式RDF存储要么不支持推理，要么只能处理有限的数据库片段。在本文中，我们将Potter等人[13]提出的分布式查询应答的动态数据交换方法扩展为一种推理算法，该算法可以处理任意规则，同时保留重要的属性，如推理的不重复性。我们还表明，我们的算法可以很好地扩展到非常大的RDF数据集。
Several centralised RDF systems support datalog reasoning by precomputing and storing all logically implied triples using the well-knownseminaïve algorithm. Large RDF datasets often exceed the capacity of centralised RDF systems, and a common solution is to distribute the datasets in a cluster of shared-nothing servers. While numerous distributed query answering techniques are known, distributed seminaïve evaluation of arbitrary datalog rules is less understood. In fact, most distributed RDF stores either support no reasoning or can handle only limited datalog fragments. In this paper, we extend thedynamic data exchangeapproach for distributed query answering by Potter et al. [13] to a reasoning algorithm that can handle arbitrary rules while preserving important properties such as nonrepetition of inferences. We also show empirically that our algorithm scales well to very large RDF datasets.