WEB-BASED DUPLICATE RECORDS DETECTION WITH ARABIC LANGUAGE ENHANCEMENT

Abstract

Sharing data between organizations has growing importance in many data mining projects. Data from various heterogeneous sources often has to be linked and aggregated in order to improve data quality. The importance of data accuracy and quality has increased with the explosion of data size. The first step to ensure the data accuracy is to make sure that each real world object is represented once and only once in a certain dataset which called Duplicate Record Detection (DRD). These data inaccuracy problems exist due to due to several factors including spelling, typographical and pronunciation variation, dialects and special vowel and consonant distinction and other linguistic characteristics especially with non-Latin languages like Arabic. In this paper, an English/Arabic enabled web-based framework is designed and implemented which considers the user interaction to add new rules, enrich the dictionary and evaluate results is an important step to improve system’s behavior. The proposed framework allows the processing on both single language dataset and bi-lingual dataset. The proposed framework is implemented and verified empirically in several case studies. The comparison results showed that the proposed system has substantial improvements compared to known tools.

Keywords

• duplicate record detection, data cleaning, indexing
• data integration, entity matching, soundex, dictionary building, similarity metrics