Finding approximate matches in large lexicons
Software—Practice & Experience
A guided tour to approximate string matching
ACM Computing Surveys (CSUR)
Real-world Data is Dirty: Data Cleansing and The Merge/Purge Problem
Data Mining and Knowledge Discovery
A Trie Compaction Algorithm for a Large Set of Keys
IEEE Transactions on Knowledge and Data Engineering
Automatically detecting deceptive criminal identities
Communications of the ACM - Homeland security
Information Policy, Data Mining, and National Security: False Positives and Unidentified Negatives
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05) - Track 5 - Volume 05
Fast Approximate Search in Large Dictionaries
Computational Linguistics
Adaptive Name Matching in Information Integration
IEEE Intelligent Systems
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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. This is an issue of grave importance in homeland security, criminology, medical applications, GIS (geographic information systems) and so on. Different techniques, such as soundex, phonix, n-grams, edit-distance, have been used to improve the matching rate in these name-matching applications. There is a pressing need for name matching approaches that provide high levels of accuracy, while at the same time maintaining the computational complexity of achieving this goal reasonably low. In this paper, we present ANSWER, a name matching approach that utilizes a prefix-tree of available names in the database. Creating and searching the name dictionary tree is fast and accurate and, thus, ANSWER is superior to other techniques of retrieving fuzzy name matches in large databases.