Download the full-sized PDF of Entity resolution for large relational datasetsDownload the full-sized PDF



Permanent link (DOI):


Export to: EndNote  |  Zotero  |  Mendeley


This file is in the following communities:

Graduate Studies and Research, Faculty of


This file is in the following collections:

Theses and Dissertations

Entity resolution for large relational datasets Open Access


Other title
Relational entity resolution
Entity resolution
Type of item
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 of Computing Science

Date accepted
Graduation date
Master of Science
Degree level
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.
License granted by Zhaochen Guo ( 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.
Citation for previous publication

File Details

Date Uploaded
Date Modified
Audit Status
Audits have not yet been run on this file.
File format: pdf (Portable Document Format)
Mime type: application/pdf
File size: 2310984
Last modified: 2015:10:12 12:15:22-06:00
Filename: zhaochen_master_thesis.pdf
Original checksum: d0eca8c53a75d829a357e02becd3d7b5
Well formed: true
Valid: false
Status message: Improperly formed date offset=2288935
File title: Introduction
File title: comparison.eps
File author: zhaochen,,,
Page count: 87
Activity of users you follow
User Activity Date