Title

Answer: Approximate Name Search With Errors In Large Databases By A Novel Approach Based On Prefix-Dictionary

Keywords

Alias finding; Data mining; Data querying; Data sharing; Dirty data; Duplicate elimination; Edit distance; Fuzzy name matching; Record matching; Soundex

Abstract

The obvious need for using modern computer networking capabilities to enable the effective sharing of information has resulted in data-sharing systems, which store, and manage large amounts of data. These data need to be effectively searched and analyzed. More specifically, in the presence of dirty data, a search for specific information by a standard query (e.g., search for a name that is misspelled or mistyped) does not return all needed information, as required in homeland security, criminology, and medical applications, amongst others. Different techniques, such as soundex, phonix, n-grams, edit-distance, have been used to improve the matching rate in these name-matching applications. These techniques have demonstrated varying levels of success, but there is a pressing need for name matching approaches that provide high levels of accuracy in matching names, while at the same time maintaining low computational complexity. In this paper, such a technique, called ANSWER, is proposed and its characteristics are discussed. Our results demonstrate that ANSWER possesses high accuracy, as well as high speed and is superior to other techniques of retrieving fuzzy name matches in large databases. © World Scientific Publishing Company.

Publication Date

10-1-2006

Publication Title

International Journal on Artificial Intelligence Tools

Volume

15

Issue

5

Number of Pages

839-848

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1142/S0218213006002977

Socpus ID

33750161464 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/33750161464

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