Enterprise RDF knowledge graphs are often built using extraction data pipelines that are fed by several heterogeneous sources (relational databases, CSV files or even unstructured textual data). As a direct consequence, the construction of these KGs undergoes a number of changes in the early stages of their life cycle, which are initiated by a human developer and therefore need to be done interactively and efficiently. Driven by such needs, in this paper, we present a solution for the incremental maintenance of KGs given user-prescribed changes. A key feature of the proposed solution is the support of provenance collection that can be used to assist the developer in the analysis and debugging of the KG. Specifically, we strive to compute and maintain the provenance of asserted and inferred facts in the knowledge graph incrementally (and thus efficiently). The evaluation exercises we have conducted show the effectiveness of our solution and highlight the parameters that impact performance.

企业 RDF 知识图通常使用提取数据管道构建，这些数据管道由多个异构源（关系数据库、CSV 文件甚至非结构化文本数据）提供。直接的结果是，这些知识图谱的构建在其生命周期的早期阶段经历了许多变化，这些变化是由人类开发人员发起的，因此需要交互式且高效地完成。在这种需求的驱动下，在本文中，我们提出了一种在用户指定的更改的情况下增量维护知识图谱的解决方案。该解决方案的一个关键特性是支持来源收集，可用于协助开发人员分析和调试知识图谱。具体来说，我们努力增量地（从而有效地）计算和维护知识图中断言和推断事实的来源。我们进行的评估练习显示了我们解决方案的有效性，并突出了影响性能的参数。