DEM:Deep Entity Matching Across Heterogeneous Information Networks  

作　　者：Chao Kong Bao-Xiang Chen Li-Ping Zhang 

机构地区：[1]School of Computer and Information,Anhui Polytechnic University,Wuhu 241000,China

出　　处：《Journal of Computer Science & Technology》2020年第4期739-750,共12页计算机科学技术学报（英文版）

基　　金：supported by the National Natural Science Foundation of China Youth Fund under Grant No.61902001.

摘　　要：Heterogeneous information networks,which consist of multi-typed vertices representing objects and multi-typed edges representing relations between objects,are ubiquitous in the real world.In this paper,we study the problem of entity matching for heterogeneous information networks based on distributed network embedding and multi-layer perceptron with a highway network,and we propose a new method named DEM short for Deep Entity Matching.In contrast to the traditional entity matching methods,DEM utilizes the multi-layer perceptron with a highway network to explore the hidden relations to improve the performance of matching.Importantly,we incorporate DEM with the network embedding methodology,enabling highly efficient computing in a vectorized manner.DEM's generic modeling of both the network structure and the entity attributes enables it to model various heterogeneous information networks flexibly.To illustrate its functionality,we apply the DEM algorithm to two real-world entity matching applications:user linkage under the social network analysis scenario that predicts the same or matched users in different social platforms and record linkage that predicts the same or matched records in different citation networks.Extensive experiments on real-world datasets demonstrate DEM's effectiveness and rationality.

关 键 词：heterogeneous information NETWORK ENTITY matching NETWORK EMBEDDING MULTI-LAYER PERCEPTRON 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]