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Graph-Boosted Active Learning for Multi-source Entity Resolution

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The Semantic Web – ISWC 2021 (ISWC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12922))

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Abstract

Supervised entity resolution methods rely on labeled record pairs for learning matching patterns between two or more data sources. Active learning minimizes the labeling effort by selecting informative pairs for labeling. The existing active learning methods for entity resolution all target two-source matching scenarios and ignore signals that only exist in multi-source settings, such as the Web of Data. In this paper, we propose ALMSER, a graph-boosted active learning method for multi-source entity resolution. To the best of our knowledge, ALMSER is the first active learning-based entity resolution method that is especially tailored to the multi-source setting. ALMSER exploits the rich correspondence graph that exists in multi-source settings for selecting informative record pairs. In addition, the correspondence graph is used to derive complementary training data. We evaluate our method using five multi-source matching tasks having different profiling characteristics. The experimental evaluation shows that leveraging graph signals leads to improved results over active learning methods using margin-based and committee-based query strategies in terms of F1 score on all tasks.

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Notes

  1. 1.

    https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.flow.minimum_cut.html.

  2. 2.

    https://github.com/wbsg-uni-mannheim/ALMSER-GB.

  3. 3.

    http://webdatacommons.org/largescaleproductcorpus/v2/.

  4. 4.

    https://sites.google.com/site/anhaidgroup/useful-stuff/data.

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Authors and Affiliations

  1. Data and Web Science Group, University of Mannheim, Mannheim, Germany

    Anna Primpeli & Christian Bizer

Authors
  1. Anna Primpeli
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  2. Christian Bizer
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Corresponding author

Correspondence to Anna Primpeli .

Editor information

Editors and Affiliations

  1. University of Würzburg, Würzburg, Germany

    Andreas Hotho

  2. Linköping University, Linköping, Sweden

    Eva Blomqvist

  3. University of Düsseldorf, Düsseldorf, Germany

    Stefan Dietze

  4. IBM Research - Thomas J. Watson Research, Hawthorne, CA, USA

    Achille Fokoue

  5. University of Texas, Austin, TX, USA

    Ying Ding

  6. Imperial College, London, UK

    Payam Barnaghi

  7. Australian National University, Canberra, ACT, Australia

    Armin Haller

  8. Fondazione Bruno Kessler, Povo, Trento, Italy

    Mauro Dragoni

  9. The Open University Walton Hall, Milton Keynes, UK

    Harith Alani

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Primpeli, A., Bizer, C. (2021). Graph-Boosted Active Learning for Multi-source Entity Resolution. In: Hotho, A., et al. The Semantic Web – ISWC 2021. ISWC 2021. Lecture Notes in Computer Science(), vol 12922. Springer, Cham. https://doi.org/10.1007/978-3-030-88361-4_11

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  • DOI: https://doi.org/10.1007/978-3-030-88361-4_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88360-7

  • Online ISBN: 978-3-030-88361-4

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