Coreference detection of low quality objects

The problem of record linkage is a widely studied problem that aims to identify coreferent (i.e. duplicate) data in a structured data source. As indicated by Winkler, a solution to the record linkage problem is only possible if the error rate is sufficiently low. In other words, in order to succesfully deduplicate a database, the objects in the database must be of sufficient quality. However, this assumption is not always feasible. In this paper, it is investigated how merging of low quality objects into one high quality object can improve the process of record linkage. This general idea is illustrated in the context of strings comparison, where strings of low quality (i.e. with a high typographical error rate) are merged into a string of high quality by using an n-dimensional Levenshtein distance matrix and compute the optimal alignment between the dirty strings. Results are presented and possible refinements are proposed

Quality and complexity measures for data linkage and deduplication

Summary. Deduplicating one data set or linking several data sets are increasingly important tasks in the data preparation steps of many data mining projects. The aim of such linkages is to match all records relating to the same entity. Research interest in this area has increased in recent years, with techniques originating from statistics, machine learning, information retrieval, and database research being combined and applied to improve the linkage quality, as well as to increase performance and efficiency when linking or deduplicating very large data sets. Different measures have been used to characterise the quality and complexity of data linkage algorithms, and several new metrics have been proposed. An overview of the issues involved in measuring data linkage and deduplication quality and complexity is presented in this chapter. It is shown that measures in the space of record pair comparisons can produce deceptive quality results. Various measures are discussed and recommendations are given on how to assess data linkage and deduplication quality and complexity. Key words: data or record linkage, data integration and matching, deduplication, data mining pre-processing, quality and complexity measures

Estimating parameters for probabilistic linkage of privacy-preserved datasets.

Background: Probabilistic record linkage is a process used to bring together person-based records from within the same dataset (de-duplication) or from disparate datasets using pairwise comparisons and matching probabilities. The linkage strategy and associated match probabilities are often estimated through investigations into data quality and manual inspection. However, as privacy-preserved datasets comprise encrypted data, such methods are not possible. In this paper, we present a method for estimating the probabilities and threshold values for probabilistic privacy-preserved record linkage using Bloom filters. Methods: Our method was tested through a simulation study using synthetic data, followed by an application using real-world administrative data. Synthetic datasets were generated with error rates from zero to 20% error. Our method was used to estimate parameters (probabilities and thresholds) for de-duplication linkages. Linkage quality was determined by F-measure. Each dataset was privacy-preserved using separate Bloom filters for each field. Match probabilities were estimated using the expectation-maximisation (EM) algorithm on the privacy-preserved data. Threshold cut-off values were determined by an extension to the EM algorithm allowing linkage quality to be estimated for each possible threshold. De-duplication linkages of each privacy-preserved dataset were performed using both estimated and calculated probabilities. Linkage quality using the F-measure at the estimated threshold values was also compared to the highest F-measure. Three large administrative datasets were used to demonstrate the applicability of the probability and threshold estimation technique on real-world data. Results: Linkage of the synthetic datasets using the estimated probabilities produced an F-measure that was comparable to the F-measure using calculated probabilities, even with up to 20% error. Linkage of the administrative datasets using estimated probabilities produced an F-measure that was higher than the F-measure using calculated probabilities. Further, the threshold estimation yielded results for F-measure that were only slightly below the highest possible for those probabilities. Conclusions: The method appears highly accurate across a spectrum of datasets with varying degrees of error. As there are few alternatives for parameter estimation, the approach is a major step towards providing a complete operational approach for probabilistic linkage of privacy-preserved datasets

Conditional Dependencies: A Principled Approach to Improving Data Quality

Abstract. Real-life date is often dirty and costs billions of pounds to businesses worldwide each year. This paper presents a promising ap-proach to improving data quality. It effectively detects and fixes inconsis-tencies in real-life data based on conditional dependencies, an extension of database dependencies by enforcing bindings of semantically related data values. It accurately identifies records from unreliable data sources by leveraging relative candidate keys, an extension of keys for relations by supporting similarity and matching operators across relations. In con-trast to traditional dependencies that were developed for improving the quality of schema, the revised constraints are proposed to improve the quality of data. These constraints yield practical techniques for data re-pairing and record matching in a uniform framework.