• 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