Enterprises are creating domain-specific knowledge graphs by curating and integrating their business data from multiple sources. The data in these knowledge graphs can be described using ontologies, which provide a semantic abstraction to define the content in terms of the entities and the relationships of the domain. The rich semantic relationships in an ontology contain a variety of opportunities to reduce edge traversals and consequently improve the graph query performance. Although there has been a lot of effort to build systems that enable efficient querying over knowledge graphs, the problem of schema optimization for query performance has been largely ignored in the graph setting. In this work, we show that graph schema design has significant impact on query performance, and then propose optimization algorithms that exploit the opportunities from the domain ontology to generate efficient property graph schemas. To the best of our knowledge, we are the first to present an ontology-driven approach for property graph schema optimization. We conduct empirical evaluations with two real-world knowledge graphs from medical and financial domains. The results show that the schemas produced by the optimization algorithms achieve up to 2 orders of magnitude speed-up compared to the baseline approach.

中文翻译：

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特定领域知识图的属性图模式优化

企业正在通过管理和集成来自多个来源的业务数据来创建特定于领域的知识图谱。这些知识图中的数据可以使用本体来描述，本体提供语义抽象以根据实体和域的关系定义内容。本体中丰富的语义关系包含各种减少边遍历的机会，从而提高图查询性能。尽管已经做了很多努力来构建能够对知识图进行高效查询的系统，但在图设置中，查询性能的模式优化问题在很大程度上被忽略了。在这项工作中，我们表明图模式设计对查询性能有显着影响，然后提出优化算法，利用领域本体的机会生成有效的属性图模式。据我们所知，我们是第一个提出用于属性图模式优化的本体驱动方法。我们使用来自医疗和金融领域的两个真实世界的知识图进行实证评估。结果表明，与基线方法相比，优化算法生成的模式实现了高达 2 个数量级的加速。