Abstract
Record linkage is the process of identifying records that refer to the same real-world entities in situations where entity identifiers are unavailable. Records are linked on the basis of similarity between common attributes, with every pair being classified as a link or non-link depending on their similarity. Linkage is usually performed in a three-step process: first, groups of similar candidate records are identified using indexing, then pairs within the same group are compared in more detail, and finally classified. Even state-of-the-art indexing techniques, such as locality sensitive hashing, have potential drawbacks. They may fail to group together some true matching records with high similarity, or they may group records with low similarity, leading to high computational overhead. We propose using metric space indexing (MSI) to perform complete linkage, resulting in a parameter-free process combining indexing, comparison and classification into a single step delivering complete and efficient record linkage. An evaluation on real-world data from several domains shows that linkage using MSI can yield better quality than current indexing techniques, with similar execution cost, without the need for domain knowledge or trial and error to configure the process.
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Notes
- 1.
Experimental data, additional figures and source code can be downloaded from: http://github.com/digitisingscotland/pakdd2018-metric-linkage.
- 2.
Relatively high Levenshtein edit distances are included since Cora contains a number of low-similarity true matches.
- 3.
Noting that recent research identifies some problematic aspects with using the F-measure to compare record linkage procedures at different similarity thresholds [14].
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Acknowledgements
This work was supported by ESRC grants ES/K00574X/2 “Digitising Scotland” and ES/L007487/1 “Administrative Data Research Centre—Scotland”.
We thank Alice Reid of the University of Cambridge and her colleagues, especially Ros Davies and Eilidh Garrett, for the work undertaken on the Kilmarnock and Isle of Skye databases.
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Akgün, Ö., Dearle, A., Kirby, G., Christen, P. (2018). Using Metric Space Indexing for Complete and Efficient Record Linkage. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10939. Springer, Cham. https://doi.org/10.1007/978-3-319-93040-4_8
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