NetOwl NameMatcher Description
NetOwl NameMatcher, recognized for its excellence in the MITRE Multicultural Name Matching Challenge, delivers unparalleled accuracy, speed, and scalability in name matching solutions. By employing an innovative machine learning framework, NetOwl effectively tackles the intricate challenges of fuzzy name matching. Conventional methods like Soundex, edit distance, and rule-based systems often face significant issues with precision, leading to false positives, and recall, resulting in false negatives, when confronting the diverse fuzzy name matching scenarios outlined previously. In contrast, NetOwl leverages a data-driven, machine learning-based probabilistic strategy to address these name matching difficulties. It automatically generates sophisticated, probabilistic name matching rules from extensive, real-world multi-ethnic name variant datasets. Furthermore, NetOwl employs distinct matching models tailored to various entity types, such as individuals, organizations, and locations. To add to its capabilities, NetOwl also integrates automatic detection of name ethnicity, enhancing its adaptability to the complexities of multicultural name matching. This comprehensive approach ensures a higher level of accuracy and reliability in diverse applications.
NetOwl NameMatcher Alternatives
DataBuck
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Web APIs by Melissa
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• Property (U.S. & Canada): Return comprehensive property & mortgage info for 140+ million U.S. properties.
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Validio
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NetOwl EntityMatcher
NetOwl EntityMatcher offers a reliable, swift, and expandable solution for identity resolution, taking into account not just the similarities in entity names but also critical attributes like date of birth, place of birth, address, and nationality. Additionally, it allows for identity resolution using social network details, including information about an individual's employer, spouse, or associates. Utilizing its proprietary search and indexing engine, NetOwl can combine evidence from various entity record attributes, providing a highly effective, scalable, and user-friendly approach to matching. Users can define application-specific business rules that dictate which combinations of record attributes need to be matched and establish the importance of each attribute. Furthermore, by incorporating our machine learning-driven multicultural and multilingual name matching solution, NetOwl NameMatcher, the system enhances the sophistication of name matching across different types of entities. This integration ensures that identity resolution maintains a high level of accuracy and adaptability in diverse scenarios.
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Integrations
Company Details
Company:
NetOwl
Year Founded:
1996
Headquarters:
United States
Website:
www.netowl.com/name-matching-software
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Product Details
Platforms
Web-Based
Types of Training
Training Docs
In Person
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Business Hours
Online Support
NetOwl NameMatcher Features and Options
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