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Semantic Data Matching: Principles and Performance

In: Data Engineering

Author

Listed:
  • Russell Deaton

    (Computer Science and Computer Engineering, University of Arkansas)

  • Thao Doan

    (Computer Science and Computer Engineering, University of Arkansas)

  • Tom Schweiger

    (Acxiom Corporation)

Abstract

Automated and real-time management of customer relationships requires robust and intelligent data matching across widespread and diverse data sources. Simple string matching algorithms, such as dynamic programming, can handle typographical errors in the data, but are less able to match records that require contextual and experiential knowledge. Latent Semantic Indexing (LSI)latent semantic indexing (LSI) (Berry et al. ; Deerwester et al. is a machine intelligence technique that can match data based upon higher order structure, and is able to handle difficult problems, such as words that have different meanings but the same spelling, are synonymous, or have multiple meanings. Essentially, the technique matches records based upon context, or mathematically quantifying when terms occur in the same record.

Suggested Citation

  • Russell Deaton & Thao Doan & Tom Schweiger, 2009. "Semantic Data Matching: Principles and Performance," International Series in Operations Research & Management Science, in: Yupo Chan & John Talburt & Terry M. Talley (ed.), Data Engineering, chapter 4, pages 77-90, Springer.
    • Handle: RePEc:spr:isochp:978-1-4419-0176-7_4
    DOI: 10.1007/978-1-4419-0176-7_4
    as

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