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Activity Number: 47 - Statistical Analysis of Linked Data
Type: Invited
Date/Time: Sunday, July 29, 2018 : 4:00 PM to 5:50 PM
Sponsor: Survey Research Methods Section
Abstract #326616 Presentation
Title: Entity Resolution with Societal Impacts in Statistical Machine Learning
Author(s): Rebecca C. Steorts*
Companies: Duke University
Keywords: Syrian conflict; entity resolution; record linkage; unique entity resolution; locality sensitive hashing

Very often information about social entities is scattered across multiple databases.  Combining that information into one database can result in enormous benefits for analysis, resulting in richer and more reliable conclusions. In practical applications, however, analysts cannot simply link records across databases based on unique identifiers, such as social security numbers, either because they are not a part of some databases or are not available due to privacy concerns.  Analysts need to use methods from statistical and computational science known as entity resolution (record linkage or de-duplication) to proceed with analysis.  Entity resolution is not only a crucial task for social science and industrial applications, but is a challenging statistical and computational problem itself. In this talk, we describe the past and present challenges with entity resolution, with applications to the Syrian conflict but also official statistics, and the food and music industry. This large collaboration touches on research that is crucial to problems with societal impacts that are at the forefront of both national and international news. 

Authors who are presenting talks have a * after their name.

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