An artificial neural network based approach for online string matching/filtering of large databases

Abstract

A novel online approach to exact string matching and filtering of large databases is presented. String matching/filtering is based on artificial neural networks and operates in two stages: initially, a self‐organizing map retrieves the cluster of database strings that are most similar to the query string; subsequently, a harmony theory network compares the retrieved strings with the query string and determines whether an exact match exists. The similarity measure is configured to the specific characteristics of the database so as to expose overall string similarity rather than character coincidence at homologous string locations. The experimental results demonstrate foolproof, fast, and practically database‐size independent operation that is especially robust to database modifications. The proposed approach is put forward for general‐purpose (directory, catalogue, glossary search) as well as Internet‐oriented (e‐mail blocking, URL, username classification) applications. © 2010 Wiley Periodicals, Inc.