Linking Entities across Relations and Graphs

This paper proposes a notion of parametric simulation to link entities across a relational database $\mathcal{D}$ and a graph $G$ . Taking functions and thresholds for measuring vertex close-ness, path associations and important properties as parameters, parametric simulation identifies tuples $t$ in $\mathcal{D}$ and vertices $v$ in $G$ that refer to the same real-world entity, based on 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. 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 $\mathcal{D}$ and $G$ , all in quadratic-time. Using real-life and synthetic data, we empirically verify that HER is accurate with $\mathbf{F}$ -measure of 0.94 on average, and is able to scale with database $\mathcal{D}$ and graph $G$ .