Abstract

Entity resolution is an important application in field of data cleaning. Standard approaches like deterministic methods and probabilistic methods are generally used for this purpose. Many new approaches using single layer perceptron, crowdsourcing etc. are developed to improve the efficiency and also to reduce the time of entity resolution. The approaches used for this purpose also depend on the type of dataset, labeled or unlabeled. This paper presents a new method for labeled data which uses single layered convolutional neural network to perform entity resolution. It also describes how crowdsourcing can be used with the output of the convolutional neural network to further improve the accuracy of the approach while minimizing the cost of crowdsourcing. The paper also discusses the data pre-processing steps used for training the convolutional neural network. Finally it describes the airplane sensor dataset which is used for demonstration of this approach and then shows the experimental results achieved using convolutional neural network.

Meeting Name

Complex Adaptive Systems (2016: Nov. 2-4, Los Angeles, CA)

Department(s)

Engineering Management and Systems Engineering

Keywords and Phrases

Adaptive systems; Convolution; Crowdsourcing; Data handling; Network layers; Neural networks; Convolutional neural network; Data preprocessing; Deterministic methods; Human Model; Probabilistic methods; Single layer perceptron; word embedding; Word-stemming; Complex networks; Hybrid machine-human model; Word stemming

International Standard Serial Number (ISSN)

1877-0509

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2016 The Authors, All rights reserved.

Creative Commons Licensing

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Publication Date

01 Nov 2016

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