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dc.contributor.authorAkgün, Özgür
dc.contributor.authorDearle, Alan
dc.contributor.authorKirby, Graham Njal Cameron
dc.contributor.authorChristen, Peter
dc.contributor.editorPhung, Dinh
dc.contributor.editorTseng, Vincent S.
dc.contributor.editorWebb, Geoff
dc.contributor.editorHo, Bao
dc.contributor.editorGanji, Mohadeseh
dc.contributor.editorRashidi, Lida
dc.identifier.citationAkgün , Ö , Dearle , A , Kirby , G N C & Christen , P 2018 , Using metric space indexing for complete and efficient record linkage . in D Phung , V S Tseng , G Webb , B Ho , M Ganji & L Rashidi (eds) , Advances in Knowledge Discovery and Data Mining : 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings, Part III . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 10939 LNCS , Springer , Cham , pp. 89-101 , 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2018) , Melbourne , Victoria , Australia , 3/06/18 .
dc.identifier.otherPURE: 252460914
dc.identifier.otherPURE UUID: 2bf3e6cc-02c5-4043-9b29-015862135919
dc.identifier.otherScopus: 85049367618
dc.identifier.otherORCID: /0000-0002-4422-0190/work/46569125
dc.identifier.otherORCID: /0000-0001-9519-938X/work/46569180
dc.description.abstractRecord 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.
dc.relation.ispartofAdvances in Knowledge Discovery and Data Miningen
dc.relation.ispartofseriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en
dc.rights© 2018, Springer. This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at
dc.subjectEntity resolutionen
dc.subjectData matchingen
dc.subjectSimilarity searchen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectZA4050 Electronic information resourcesen
dc.subjectTheoretical Computer Scienceen
dc.subjectComputer Science(all)en
dc.titleUsing metric space indexing for complete and efficient record linkageen
dc.typeConference itemen
dc.contributor.sponsorEconomic & Social Research Councilen
dc.contributor.sponsorEconomic & Social Research Councilen
dc.contributor.sponsorScottish Funding Councilen
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.contributor.institutionUniversity of St Andrews. Office of the Principalen
dc.contributor.institutionUniversity of St Andrews. Centre for Interdisciplinary Research in Computational Algebraen

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