Morph-KGC: Scalable knowledge graph materialization with mapping partitions
Morph-KGC: Scalable knowledge graph materialization with mapping partitions
复制标题
Morph-KGC：具有映射分区的可扩展知识图具体化
DOI：
10.3233/sw-223135
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发表时间：
2022
期刊：
影响因子：
3
通讯作者：
Óscar Corcho
中科院分区：
文献类型：
--
作者：
Julián Arenas;David Chaves;Jhon Toledo;María S. Pérez;Óscar Corcho
知识图通常是使用异质数据源构建的，使用声明性规则将其映射到目标本体，并将其实现为RDF。当这些数据源很大时，整个知识图的实现在计算上可能很昂贵，并且不适用于需要快速实现的情况。在这项工作中，我们提出了一种基于映射分区的新概念来克服这种局限性的方法。映射分区定义为映射规则的组，这些规则产生了知识图的不相交子集。这些组中的每一个都可以分开处理，从而减少了物质化过程所需的内存和执行时间。我们已经将这种优化在我们的物质化引擎变形KGC中包括在内，并且在三个不同的基准测试中进行了评估。我们的实验结果表明，与最先进的技术相比，在变形-KGC中的映射分区的使用具有以下优点：（i）它大大减少了物质化所需的时间，（ii）它减少了最大值。所使用的内存峰值，（iii）它扩展到当前其他发动机无法处理的数据大小。
Knowledge graphs are often constructed from heterogeneous data sources, using declarative rules that map them to a target ontology and materializing them into RDF. When these data sources are large, the materialization of the entire knowledge graph may be computationally expensive and not suitable for those cases where a rapid materialization is required. In this work, we propose an approach to overcome this limitation, based on the novel concept of mapping partitions. Mapping partitions are defined as groups of mapping rules that generate disjoint subsets of the knowledge graph. Each of these groups can be processed separately, reducing the total amount of memory and execution time required by the materialization process. We have included this optimization in our materialization engine Morph-KGC, and we have evaluated it over three different benchmarks. Our experimental results show that, compared with state-of-the-art techniques, the use of mapping partitions in Morph-KGC presents the following advantages: (i) it decreases significantly the time required for materialization, (ii) it reduces the maximum peak of memory used, and (iii) it scales to data sizes that other engines are not capable of processing currently.