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