DocumentCode :
1791596
Title :
Entity resolution using inferred relationships and behavior
Author :
Mugan, Jonathan ; Chari, Ranga ; Hitt, Laura ; McDermid, Eric ; Sowell, Marsha ; Yuan Qu ; Coffman, Thayne
Author_Institution :
21CT, Inc., Austin, TX, USA
fYear :
2014
fDate :
27-30 Oct. 2014
Firstpage :
555
Lastpage :
560
Abstract :
We present a method for entity resolution that infers relationships between observed identities and uses those relationships to aid in mapping identities to underlying entities. We also introduce the idea of using graphlets for entity resolution. Graphlets are collections of small graphs that can be used to characterize the “role” of a node in a graph. The idea is that graphlets can provide a richer set of features to characterize identities. We validate our method on standard author datasets, and we further evaluate our method using data collected from Twitter. We find that inferred relationships and graphlets are useful for entity resolution.
Keywords :
data mining; graphs; information retrieval; social networking (online); Twitter; entity resolution; graphlets; inferred relationships; small graphs; standard author datasets; Biological cells; Facebook; Genetic algorithms; Optimization; Orbits; Twitter; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Big Data (Big Data), 2014 IEEE International Conference on
Conference_Location :
Washington, DC
Type :
conf
DOI :
10.1109/BigData.2014.7004273
Filename :
7004273
Link To Document :
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