Efficient interactive training selection for large-scale entity resolution

Qing Wang*, Dinusha Vatsalan, Peter Christen

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    15 Citations (Scopus)


    Entity resolution (ER) has wide-spread applications in many areas, including e-commerce, health-care, the social sciences, and crime and fraud detection. A crucial step in ER is the accurate classification of pairs of records into matches (assumed to refer to the same entity) and non-matches (assumed to refer to different entities). In most practical ER applications it is difficult and costly to obtain training data of high quality and enough size, which impedes the learning of an ER classifier. We tackle this problem using an interactive learning algorithm that exploits the cluster structure in similarity vectors calculated from compared record pairs. We select informative training examples to assess the purity of clusters, and recursively split clusters until clusters pure enough for training are found. We consider two aspects of active learning that are significant in practical applications: a limited budget for the number of manual classifications that can be done, and a noisy oracle where manual labeling might be incorrect. Experiments using several real data sets show that manual labeling efforts can be significantly reduced for training an ER classifier without compromising matching quality.

    Original languageEnglish
    Title of host publicationAdvances in Knowledge Discovery and Data Mining - 19th Pacific-Asia Conference, PAKDD 2015, Proceedings
    EditorsTru Cao, Ee-Peng Lim, Tu-Bao Ho, Zhi-Hua Zhou, Hiroshi Motoda, David Cheung
    PublisherSpringer Verlag
    Number of pages12
    ISBN (Print)9783319180311
    Publication statusPublished - 2015
    Event19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2015 - Ho Chi Minh City, Viet Nam
    Duration: 19 May 201522 May 2015

    Publication series

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


    Conference19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2015
    Country/TerritoryViet Nam
    CityHo Chi Minh City


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