An Iterative, Self-Assessing Entity Resolution System: First Steps toward a Data Washing Machine

• Published in: International journal of advanced computer science & applications 2020-12, Vol.11 (12)
• Main Authors: Talburt, John R., K., Awaad, Pullen, Daniel, Claassens, Leon, Wang, Richard
• Format: Article
• Language: English
• Subjects: Automation; Computer science; Critical components; Data acquisition; Data integration; Iterative methods; Layouts; Metadata; Quality assessment; Recall; Reference materials; Washers & dryers; Washing machines
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2020.0111279

Data curation is the process of acquiring multiple sources of data, assessing and improving data quality, standardizing, and integrating the data into a usable information product, and eventually disposing of the data. The research describes the building of a proof-of-concept for an unsupervised data curation process addressing a basic form of data cleansing in the form of identifying redundant records through entity resolution and spelling corrections. The novelty of the approach is to use ER as the first step using an unsupervised blocking and stop word scheme based on token frequency. A scoring matrix is used for linking unstandardized references, and an unsupervised process for evaluating linking results based on cluster entropy. The ER process is iterative, and in each iteration, the match threshold is increased. The prototype was tested on 18 fully-annotated test samples of primarily synthetic person data varied in two different ways, good data quality versus poor data quality, and a single record layout versus two different record layouts. In samples with good data quality and using both single and mixed layouts, the final clusters had an average F-measure of 0.91, precision of 0.96, and recall of 0.87 outcomes comparable to results from a supervised ER process. In samples with poor data quality whether mixed or single layout, the average F-measure was 0.78, precision 0.74, and recall 0.83 showing that data quality assessment and improvement is still a critical component of successful data curation. The results demonstrate the feasibility of building an unsupervised ER engine to support data integration for good quality references while avoiding the time and effort to standardize reference sources to a common layout, design, and test matching rules, design blocking keys, or test blocking alignment. Also, the paper proposes how unsupervised data quality improvement processes could also be incorporated into the design allowing the model to address an even broader range of data curation applications.