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2022 | OriginalPaper | Chapter

14. Graph-Based Hierarchical Record Clustering for Unsupervised Entity Resolution

Authors : Islam Akef Ebeid, John R. Talburt, Md Abdus Salam Siddique

Published in: ITNG 2022 19th International Conference on Information Technology-New Generations

Publisher: Springer International Publishing

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Abstract

The chapter discusses the critical problem of entity resolution in data cleaning, curation, and integration, focusing on unsupervised methods. It introduces a graph-based hierarchical record clustering technique, GDWM, which modifies the Data Washing Machine (DWM) algorithm to enhance accuracy and efficiency. The method integrates graph-based transitive closure and Modularity optimization, eliminating the need for iterative reiterations and threshold setting. The chapter also presents experiments on synthetic benchmark datasets, demonstrating the effectiveness and superior performance of GDWM compared to the original DWM. The results highlight significant improvements in precision, recall, F1 scores, and execution time, making GDWM a standout solution in the field of unsupervised entity resolution.
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Metadata
Title
Graph-Based Hierarchical Record Clustering for Unsupervised Entity Resolution
Authors
Islam Akef Ebeid
John R. Talburt
Md Abdus Salam Siddique
Copyright Year
2022
Book
ITNG 2022 19th International Conference on Information Technology-New Generations

Print ISBN: 978-3-030-97651-4

Electronic ISBN: 978-3-030-97652-1

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-3-030-97652-1_14

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