论文标题
从知识图中解决采矿路径模式中的可伸缩性问题：初步研究
Tackling scalability issues in mining path patterns from knowledge graphs: a preliminary study
论文作者
论文摘要
从知识图挖掘的功能被广泛用于多个知识发现任务，例如分类或事实检查。在这里，我们考虑了一组称为种子顶点的顶点，并专注于挖掘其相关的相邻顶点，路径以及更一般而言的路径模式，这些路径模式涉及与知识图相关的本体类别。由于合并性质和实际知识图的规模不断增加，挖掘这些模式的任务立即需要可伸缩性问题。在本文中，我们通过提出一种依赖一组约束（例如，支持或学位阈值）和单调性属性的模式挖掘方法来解决这些问题。由于我们的动机来自现实世界知识图的采矿，我们用生物医学知识图PGXLOD说明了我们的方法。
Features mined from knowledge graphs are widely used within multiple knowledge discovery tasks such as classification or fact-checking. Here, we consider a given set of vertices, called seed vertices, and focus on mining their associated neighboring vertices, paths, and, more generally, path patterns that involve classes of ontologies linked with knowledge graphs. Due to the combinatorial nature and the increasing size of real-world knowledge graphs, the task of mining these patterns immediately entails scalability issues. In this paper, we address these issues by proposing a pattern mining approach that relies on a set of constraints (e.g., support or degree thresholds) and the monotonicity property. As our motivation comes from the mining of real-world knowledge graphs, we illustrate our approach with PGxLOD, a biomedical knowledge graph.
