A Vertex Matcher for Entity Resolution on Graphs

Abstract

Entity resolution (ER) is widely studied and a well-defined problem, often used for data management. ER identify and merge the redundant mentions of the same entity across multiple datasets. ER has been addressed with a variety of supervised, unsupervised, and probabilistic approaches to maintain data quality and reliability. ER is crucial and yet very useful for resolving records that refer to the same real-world entity. Traditionally, pairwise comparisons have been used for matching entities across databases, which is a computation-intensive task. Also, these approaches usually do not consider the structural similarity between the records during the comparison. To address these challenges, we proposed a vertex matcher (vMatcher), a graph-based approach that effectively represents the structural similarity between the entities and match entities only in their neighborhood that significantly improve the performance and efficiency. Also, we will learn the threshold value for matching similarity by a subset of training data in a supervised manner.