Unsupervised record matching with noisy and incomplete data

van Gennip, Yves; Hunter, Blake; Ma, Anna; Moyer, Dan; de Vera, Ryan; Bertozzi, Andrea L.

Abstract

We consider the problem of duplicate detection in noisy and incomplete data: given a large data set in which each record has multiple entries (attributes), detect which distinct records refer to the same real world entity. This task is complicated by noise (such as misspellings) and missing data, which can lead to records being different, despite referring to the same entity. Our method consists of three main steps: creating a similarity score between records, grouping records together into "unique entities", and refining the groups. We compare various methods for creating similarity scores between noisy records, considering different combinations of string matching, term frequency-inverse document frequency methods, and n-gram techniques. In particular, we introduce a vectorized soft term frequency-inverse document frequency method, with an optional refinement step. We also discuss two methods to deal with missing data in computing similarity scores.

We test our method on the Los Angeles Police Department Field Interview Card data set, the Cora Citation Matching data set, and two sets of restaurant review data. The results show that the methods that use words as the basic units are preferable to those that use 3-grams. Moreover, in some (but certainly not all) parameter ranges soft term frequency-inverse document frequency methods can outperform the standard term frequency-inverse document frequency method. The results also confirm that our method for automatically determining the number of groups typically works well in many cases and allows for accurate results in the absence of a priori knowledge of the number of unique entities in the data set.

Citation

van Gennip, Y., Hunter, B., Ma, A., Moyer, D., de Vera, R., & Bertozzi, A. L. (2018). Unsupervised record matching with noisy and incomplete data. International Journal of Data Science and Analytics, 6(2), 109–129. https://doi.org/10.1007/s41060-018-0129-7

• Journal Article Type: Article
• Acceptance Date: Apr 30, 2018
• Online Publication Date: May 23, 2018
• Publication Date: 2018-09
• Deposit Date: Apr 30, 2018
• Publicly Available Date: May 24, 2019
• Journal: International Journal of Data Science and Analytics
• Electronic ISSN: 2364-415X
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 6
• Issue: 2
• Pages: 109–129
• DOI: https://doi.org/10.1007/s41060-018-0129-7
• Keywords: duplicate detection; data cleaning; data integration; record linkage; entity matching; identity uncertainty; transcription error
• Public URL: https://nottingham-repository.worktribe.com/output/933965
• Publisher URL: https://link.springer.com/article/10.1007/s41060-018-0129-7
• Additional Information: This is a post-peer-review, pre-copyedit version of an article published in International Journal of Data Science and Analytics. The final authenticated version is available online at: http://dx.doi.org/10.1007/s41060-018-0129-7
• Contract Date: Apr 30, 2018