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Permanent link (DOI): https://doi.org/10.7939/R3RS8N

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Entity resolution for large relational datasets Open Access

Descriptions

Other title
Subject/Keyword
Relational entity resolution
Entity resolution
Scalability
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Guo, Zhaochen
Supervisor and department
Denilson Barbosa (Computing Science)
Examining committee member and department
Marek Reformat (Electrical and Computer Engineering)
Mario Nascimento (Computing Science)
Department
Department of Computing Science
Specialization

Date accepted
2010-01-08T22:03:04Z
Graduation date
2010-06
Degree
Master of Science
Degree level
Master's
Abstract
As the volume of data on the Web or in databases increases, data integration is becoming more expensive and challenging than ever before. One of the challenges is entity resolution when integrating data from different sources. References with different representations but referring to the same underlying entity need to be resolved. And, references with similar descriptions but referring to different entities need to be distinguished from one another. Correctly de-duplicating and disambiguating these entities is an essential task in preparing high quality data. Traditional approaches mainly focus on the attribute similarity of references, but they do not always work for datasets with insufficient information. However, in relational datasets like social networks, references are always associated with one or more relationships and these relationships can provide additional information for identifying duplicates. In this thesis, we solve the entity resolution problem by using relationships in the relational datasets. We implement a relational entity resolution algorithm to resolve entities based on an existing algorithm, greatly improving its efficiency and performance. Also, we generalize the single-type entity resolution algorithm to a multi-type entity resolution algorithm for applications that require to resolve multiple types of reference simultaneously and demonstrate its advantage over the single-type entity resolution algorithm. To improve the efficiency of the entity resolution process, we implement two blocking approaches to reduce the number of redundant comparisons performed by other methods. In addition, we implement a disk-based clustering algorithm that addresses the scalability problem, and apply it on a large academic social network dataset.
Language
English
DOI
doi:10.7939/R3RS8N
Rights
License granted by Zhaochen Guo (zhaochen@ualberta.ca) on 2010-01-07T15:59:56Z (GMT): Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of the above terms. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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