This article proposes a notion of parametric simulation to link entities across a relational database 𝒟 and a graph G. Taking functions and thresholds for measuring vertex closeness, path associations, and important properties as parameters, parametric simulation identifies tuples t in 𝒟 and vertices v in G that refer to the same real-world entity, based on both topological and semantic matching. We develop machine learning methods to learn the parameter functions and thresholds. We show that parametric simulation is in quadratic-time by providing such an algorithm. Moreover, we develop an incremental algorithm for parametric simulation; we show that the incremental algorithm is bounded relative to its batch counterpart, i.e., it incurs the minimum cost for incrementalizing the batch algorithm. Putting these together, we develop HER, a parallel system to check whether (t, v) makes a match, find all vertex matches of t in G, and compute all matches across 𝒟 and G, all in quadratic-time; moreover, HER supports incremental computation of these in response to updates to 𝒟 and G. Using real-life and synthetic data, we empirically verify that HER is accurate with F-measure of 0.94 on average, and is able to scale with database 𝒟 and graph G for both batch and incremental computations.

本文提出了参数模拟的概念，用于跨关系数据库 𝒟 和图G链接实体。以测量顶点紧密度、路径关联和重要属性的函数和阈值作为参数，参数化模拟基于拓扑和语义匹配来识别𝒟 中的元组t和G中的顶点v，它们引用同一现实世界实体。我们开发机器学习方法来学习参数函数和阈值。通过提供这样的算法，我们证明了参数仿真是二次时间的。此外，我们开发了一种用于参数模拟的增量算法；我们表明，增量算法相对于其批处理算法是有界的，即增量批处理算法的成本最小。将这些放在一起，我们开发她，一个并行系统，用于检查 ( t, v ) 是否匹配，查找G中t的所有顶点匹配，并计算 𝒟 和G之间的所有匹配，所有这些都在二次时间内完成；而且，她支持增量计算以响应 𝒟 和G的更新。使用现实生活和合成数据，我们凭经验验证她准确度平均为 0.94，并且能够通过数据库 𝒟 和图G进行扩展，以进行批量和增量计算。