Active learning based entity resolution using Markov logic

Jeffrey Fisher*, Peter Christen, Qing Wang

*Corresponding author for this work

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

    11 Citations (Scopus)

    Abstract

    Entity resolution is a common data cleaning and data integration problem that involves determining which records in one or more data sets refer to the same real-world entities. It has numerous applications for commercial, academic and government organisations. For most practical entity resolution applications, training data does not exist which limits the type of classification models that can be applied. This also prevents complex techniques such as Markov logic networks from being used on real-world problems. In this paper we apply an active learning based technique to generate training data for a Markov logic network based entity resolution model and learn the weights for the formulae in a Markov logic network. We evaluate our technique on realworld data sets and show that we can generate balanced training data and learn and also learn approximate weights for the formulae in the Markov logic network.

    Original languageEnglish
    Title of host publicationAdvances in Knowledge Discovery and Data Mining - 20th Pacific-Asia Conference, PAKDD 2016, Proceedings
    EditorsJames Bailey, Latifur Khan, Takashi Washio, Gillian Dobbie, Joshua Zhexue Huang, Ruili Wang
    PublisherSpringer Verlag
    Pages338-349
    Number of pages12
    ISBN (Print)9783319317496
    DOIs
    Publication statusPublished - 2016
    Event20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016 - Auckland, New Zealand
    Duration: 19 Apr 201622 Apr 2016

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume9652 LNAI
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016
    Country/TerritoryNew Zealand
    CityAuckland
    Period19/04/1622/04/16

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