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Deep Web Entity Identification Method with Unique Constraint

Volume 14, Number 10, October 2018, pp. 2470-2482
DOI: 10.23940/ijpe.18.10.p23.24702482

Xuefeng Xiana, Pengpeng Zhaob, Zhaobin Liua, Caidong Gua, and Victor S. Shengc

aSchool of Computer Engineering, Suzhou Vocational University, Suzhou, 215104, China
bThe Institute of Intelligent Information Processing and Application, Soochow University, Suzhou, 215006, China
cComputer Science Department, University of Central Arkansas, Conway, 72035, USA

(Submitted on July 5, 2018; Revised on August 8, 2018; Accepted on September 15, 2018)

Abstract:

In practice, some attributes meet a unique constraint: each entity has a unique value for the attribute. A deep web entity identification method was presented to solve problems of data error correction, uniqueness constraint enforcement, and local data fusion in deep web data integration. The method transformed the entity identification phrase to a k-partite graph clustering problem, considering both similarity and association of attribute values. Moreover, it performed global record linkage and data fusion simultaneously and could identify incorrect values and differentiate them from correct ones at the beginning. Experimental results demonstrate the high precision and scalability of our method.

 

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