Supervised machine learning techniques for data matching based on similarity metrics

• URL: http://arxiv.org/abs/2007.04001v2
• Date: Wed, 15 Sep 2021 12:46:07 GMT
• Title: Supervised machine learning techniques for data matching based on similarity metrics
• Authors: Pim Verschuuren, Serena Palazzo, Tom Powell, Steve Sutton, Alfred Pilgrim, Michele Faucci Giannelli
• Abstract summary: Data matching is the field that tries to identify instances in data that refer to the same real-world entity. In this study, machine learning techniques are combined with string similarity functions to the field of data matching. The performance was compared with a solution from FISCAL Technologies as a benchmark against currently available deduplication solutions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Businesses, governmental bodies and NGO's have an ever-increasing amount of data at their disposal from which they try to extract valuable information. Often, this needs to be done not only accurately but also within a short time frame. Clean and consistent data is therefore crucial. Data matching is the field that tries to identify instances in data that refer to the same real-world entity. In this study, machine learning techniques are combined with string similarity functions to the field of data matching. A dataset of invoices from a variety of businesses and organizations was preprocessed with a grouping scheme to reduce pair dimensionality and a set of similarity functions was used to quantify similarity between invoice pairs. The resulting invoice pair dataset was then used to train and validate a neural network and a boosted decision tree. The performance was compared with a solution from FISCAL Technologies as a benchmark against currently available deduplication solutions. Both the neural network and boosted decision tree showed equal to better performance.

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