NetOwl NameMatcher

Name Matching

NetOwl NameMatcher, the winner of the MITRE Multicultural Name Matching Challenge, is the best-of-breed intelligent name matching software that addresses the needs of many critical applications. These applications call for accurate and fast matching of names expressed by varying transliterations and spellings. For example, name matching is used for:

  • Homeland security and counter terrorism (e.g., terrorist watch lists, visa screening)
  • Regulatory compliance (e.g., Know Your Customer, Politically Exposed People, OFAC)
  • Anti-money Laundering (AML)
  • Customer Relationship Management (CRM)

Name Matching Challenges

Name matching against a database of names, especially a large-scale, multi-ethnicity name database, poses serious challenges for many reasons including:

  • multiple transliteration variants of foreign names  (Abdel Fattah el-Sisi Abdul Fatah al-Sisi)
  • name order variations  (Park Sol MiSol Mi Park)
  • nicknames  (EdwardTed Teddy)
  • initials  (John Fitzgerald Kennedy J. F. Kennedy)
  • tokenization variations  (Abd al-Aziz  –  Abdul Aziz)
  • orthographic variations  (Teng Hsiaoping  –  Teng Hsiao-P’ing)
  • missing/optional name tokens  (Joaquín Archivaldo Guzmán LoeraJoaquín Guzmán)
  • misspellings  (Barack Obama  –  Barak Obama)
  • abbreviations  (Hewlett PackardHP)
  • alternate names  (Mumbai Bombay)
  • names in different languages  (Hu Jintao胡锦涛胡錦濤 Ху Цзиньтао –  هو جين تاو)
  • etc.

Traditional approaches to the name matching problem suffer from a precision problem (extraneous matches) as well as a recall problem (missing matches).

Revolutionary Machine Learning-based Approach

With its unique, empirically driven, machine learning-based probabilistic approach, NameMatcher provides highly accurate, scalable, and flexible automated name matching for many languages and ethnicities.

NetOwl supports name matching for many entity types:

  • Person
  • Place
  • Organization
  • Address
  • Vehicle
  • Email
  • Nationality
  • Phone

NetOwl NameMatcher uses optimized matching models for each entity type to achieve state-of-the-art accuracy. Additionally, NameMatcher automatically detects name ethnicities that aid in achieving this level of performance.

  • Accurate

    Achieves high accuracy using intelligent, probabilistic, name matching rules derived from large-scale, real-world, multi-ethnicity name variant data.
  • Fast

    Performs name matching against tens of millions or more names with sub-second response times.
  • Scalable

    Highly scalable name matching software for Big Data environments.
  • Multilingual

    Supports multiple languages including:
    • Languages in Latin-based alphabets
    • Arabic
    • Chinese (traditional and simplified)
    • Korean
    • Persian (Farsi and Dari)
    • Languages in Cyrillic alphabets
  • Cross-lingual

    Supports matching of a name in one language against names in other languages.
  • Customizable

    Allows parameter tuning as well as addition of custom rules and dictionaries.

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