Entity Disambiguation 
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Matthias Blume / CoreLogic
 
Abstract 
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Entity disambiguation (a.k.a. Entity Resolution, Record Linking,
People Search, Customer Pinning, Merge/Purge, ...)  determines which
data records correspond to distinct entities (persons, companies,
locations, etc.) when IDs such as SSN are not available.  Matthias
will present an overview of the field and a technique that can utilize
any available attributes including co-occurring entities, relations,
and topics from unstructured text.  It automatically learns the
information value of each feature from the data.  By using a greedy
merge approach and some tricks to avoid unnecessary match operations,
it is fast.  Finally, we will explore possible vector space and graph
representations of the problem, alternative approaches that have been
tried, and suggest future work based on reinforcement learning and
active learning.
 
Bio
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Matthias Blume is Senior Director of Analytics at CoreLogic, the
nation's largest real estate data provider.  His team develops
solutions for mortgage fraud detection, consumer credit scoring,
automated valuation models, and more.  Previously, he worked in
marketing optimization, text analytics, and the gamut of financial
services analytics at Redlign, Covario, and HNC/FICO.  He received his
PhD in Electrical and Computer Engineering from UCSD and a BS from
Caltech.