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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

ModER: Graph-based Unsupervised Entity Resolution using Composite Modularity Optimization and Locality Sensitive Hashing

Author 1: Islam Akef Ebeid
Author 2: John R. Talburt
Author 3: Nicholas Kofi Akortia Hagan
Author 4: Md Abdus Salam Siddique

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.0130901

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 9, 2022.

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Abstract: Entity resolution describes techniques used to identify documents or records that might not be duplicated; nevertheless, they might refer to the same entity. Here we study the problem of unsupervised entity resolution. Current methods rely on human input by setting multiple thresholds prior to execution. Some methods also rely on computationally expensive similarity metrics and might not be practical for big data. Hence, we focus on providing a solution, namely ModER, capable of quickly identifying entity profiles in ambiguous datasets using a graph-based approach that does not require setting a matching threshold. Our framework exploits the transitivity property of approximate string matching across multiple documents or records. We build on our previous work in graph-based unsupervised entity resolution, namely the Data Washing Machine (DWM) and the Graph-based Data Washing Machine (GDWM). We provide an extensive evaluation of a synthetic data set. We also benchmark our proposed framework using state-of-the-art methods in unsupervised entity resolution. Furthermore, we discuss the implications of the results and how it contributes to the literature.

Keywords: Entity resolution; data curation; database; graph theory; natural language processing

Islam Akef Ebeid, John R. Talburt, Nicholas Kofi Akortia Hagan and Md Abdus Salam Siddique, “ModER: Graph-based Unsupervised Entity Resolution using Composite Modularity Optimization and Locality Sensitive Hashing” International Journal of Advanced Computer Science and Applications(IJACSA), 13(9), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130901

@article{Ebeid2022,
title = {ModER: Graph-based Unsupervised Entity Resolution using Composite Modularity Optimization and Locality Sensitive Hashing},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130901},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130901},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {9},
author = {Islam Akef Ebeid and John R. Talburt and Nicholas Kofi Akortia Hagan and Md Abdus Salam Siddique}
}


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