A Graduate-Level Course on Entity Resolution and Information Quality: A Step toward ER Education

Authors:
Yinle Zhou;Eric Nelson;Fumiko Kobayashi;John R. Talburt
Affiliations:
University of Arkansas at Little Rock;University of Arkansas at Little Rock;University of Arkansas at Little Rock;University of Arkansas at Little Rock
Venue:
Journal of Data and Information Quality (JDIQ) - Special Issue on Entity Resolution
Year:
2013

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Abstract

This article discusses the topics, approaches, and lessons learned in teaching a graduate-level course covering entity resolution (ER) and its relationship to information quality (IQ). The course surveys a broad spectrum of ER topics and activities including entity reference extraction, entity reference preparation, entity reference resolution techniques, entity identity management, and entity relationship analysis. The course content also attempts to balance aspects of ER theory with practical application through a series of laboratory exercises coordinated with the lecture topics. As an additional teaching aid, a configurable, open-source entity resolution engine (OYSTER) was developed that allows students to experience with different types of ER architectures including merge-purge, record linking, identity resolution, and identity capture.