Record linkage refers to the task of finding and linking records (in a single database or in a set of data sources) that refer to the same entity. Automating the record linkage process is a challenging problem, and has been the topic of extensive research for many years. However, the changing nature of the linkage process and the growing size of data sources create new challenges for this task.
This thesis studies the record linkage problem for Web data sources. Our hypothesis is that a generic and extensible set of linkage algorithms combined within an easy-to-use framework that integrates and allows tailoring and combining of these algorithms can be used to effectively link large collections of Web data from different domains.
To this end, we first present a framework for record linkage over relational data, motivated by the fact that many Web data sources are powered by relational database engines. This framework is based on declarative specification of the linkage requirements by the user and allows linking records in many real-world scenarios. We present algorithms for translation of these requirements to queries that can run over a relational data source, potentially using a semantic knowledge base to enhance the accuracy of link discovery.
Effective specification of requirements for linking records across multiple data sources requires understanding the schema of each source, identifying attributes that can be used for linkage, and their corresponding attributes in other sources. Schema or attribute matching is often done with the goal of aligning schemas, so attributes are matched if they play semantically related roles in their schemas. In contrast, we seek to find attributes that can be used to link records between data sources, which we refer to as linkage points. In this thesis, we define the notion of linkage points and present the first linkage point discovery algorithms.
We then address the novel problem of how to publish Web data in a way that facilitates record linkage. We hypothesize that careful use of existing, curated Web sources (their data and structure) can guide the creation of conceptual models for semi-structured Web data that in turn facilitate record linkage with these curated sources. Our solution is an end-to-end framework for data transformation and publication, which includes novel algorithms for identification of entity types and their relationships out of semi-structured Web data. A highlight of this thesis is showcasing the application of the proposed algorithms and frameworks in real applications and publishing the results as high-quality data sources on the Web.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/36281 |
| Date | 15 August 2013 |
| Creators | Hassanzadeh, Oktie |
| Contributors | Miller, Renee J. |
| Source Sets | University of Toronto |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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