Linking Entities across Relations and Graphs

Abstract

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 closeness, 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 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 \(\mathcal {D} \) and G, all in quadratic-time; moreover, HER supports incremental computation of these in response to updates to \(\mathcal {D} \) 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 \(\mathcal {D} \) and graph G for both batch and incremental computations.