Linking Entities across Relations and Graphs
Wenfei Fan, Liang Geng, Ruochun Jin, Ping Lü, Resul Tugay, Wenyuan Yu · 2022 IEEE 38th International Conference on Data Engineering (ICDE) · 2022
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$G$.