Linking Entities across Relations and Graphs
Article No.: 2, Pages 1 - 50
Abstract
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.
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Publication History
Published: 28 February 2024
Online AM: 03 January 2024
Accepted: 05 December 2023
Revised: 29 August 2023
Received: 28 August 2022
Published in TODS Volume 49, Issue 1
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