Using metric space indexing for complete and efficient record linkage

Özgür Akgün*, Alan Dearle, Graham Njal Cameron Kirby, Peter Christen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)
2 Downloads (Pure)

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.
Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
Subtitle of host publication22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings, Part III
EditorsDinh Phung, Vincent S. Tseng, Geoff Webb, Bao Ho, Mohadeseh Ganji, Lida Rashidi
Place of PublicationCham
PublisherSpringer
Pages89-101
Number of pages13
ISBN (Electronic)9783319930404
ISBN (Print)9783319930398
DOIs
Publication statusPublished - 2018
Event22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2018) - Melbourne, Australia
Duration: 3 Jun 20186 Jun 2018
Conference number: 22
http://prada-research.net/pakdd18/

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer
Volume10939 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2018)
Abbreviated titlePAKDD 2018
Country/TerritoryAustralia
CityMelbourne
Period3/06/186/06/18
Internet address

Keywords

  • Entity resolution
  • Data matching
  • Similarity search
  • Blocking

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