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International Semantic Web Conference

ISWC 2021: The Semantic Web – ISWC 2021 pp 56–73Cite as

Towards Neural Schema Alignment for OpenStreetMap and Knowledge Graphs

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

Abstract

OpenStreetMap (OSM) is one of the richest, openly available sources of volunteered geographic information. Although OSM includes various geographical entities, their descriptions are highly heterogeneous, incomplete, and do not follow any well-defined ontology. Knowledge graphs can potentially provide valuable semantic information to enrich OSM entities. However, interlinking OSM entities with knowledge graphs is inherently difficult due to the large, heterogeneous, ambiguous, and flat OSM schema and the annotation sparsity. This paper tackles the alignment of OSM tags with the corresponding knowledge graph classes holistically by jointly considering the schema and instance layers. We propose a novel neural architecture that capitalizes upon a shared latent space for tag-to-class alignment created using linked entities in OSM and knowledge graphs. Our experiments aligning OSM datasets for several countries with two of the most prominent openly available knowledge graphs, namely, Wikidata and DBpedia, demonstrate that the proposed approach outperforms the state-of-the-art schema alignment baselines by up to 37% points F1-score. The resulting alignment facilitates new semantic annotations for over 10 million OSM entities worldwide, which is over a 400% increase compared to the existing annotations.

Keywords

  • OpenStreetMap
  • Knowledge graph
  • Neural schema alignment

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Notes

  1. 1.

    OSM “How to map a”: https://wiki.openstreetmap.org/wiki/How_to_map_a.

  2. 2.

    GitHub repository: https://github.com/alishiba14/NCA-OSM-to-KGs.

  3. 3.

    OSM taginfo: https://taginfo.openstreetmap.org/tags.

  4. 4.

    OSM wiki: https://wiki.openstreetmap.org/wiki/.

  5. 5.

    Wikidata “OpenStreetMap tag or key” property: https://www.wikidata.org/wiki/Property:P1282.

  6. 6.

    https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.en.300.bin.gz.

  7. 7.

    Delftdata GitHub repository: https://github.com/delftdata/valentine.

  8. 8.

    WorldKG knowledge graph: http://www.worldkg.org.

  9. 9.

    https://www.w3.org/wiki/SweoIG/TaskForces/CommunityProjects/LinkingOpenData.

  10. 10.

    OAEI evaluation campaigns: http://oaei.ontologymatching.org.

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Acknowledgements

This work was partially funded by DFG, German Research Foundation (“WorldKG”, DE 2299/2-1), BMBF, Germany (“Simple-ML”, 01IS18054) and BMWi, Germany (“d-E-mand”, 01ME19009B).

Author information

Authors and Affiliations

  1. Data Science and Intelligent Systems (DSIS), University of Bonn, Bonn, Germany

    Alishiba Dsouza & Elena Demidova

  2. L3S Research Center, Leibniz Universität Hannover, Hannover, Germany

    Nicolas Tempelmeier

Authors
  1. Alishiba Dsouza
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  2. Nicolas Tempelmeier
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  3. Elena Demidova
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Corresponding author

Correspondence to Alishiba Dsouza .

Editor information

Editors and Affiliations

  1. University of Würzburg, Würzburg, Germany

    Prof. Dr. Andreas Hotho

  2. Linköping University, Linköping, Sweden

    Assoc. Prof. 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

    Dr. Mauro Dragoni

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

    Harith Alani

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Dsouza, A., Tempelmeier, N., Demidova, E. (2021). Towards Neural Schema Alignment for OpenStreetMap and Knowledge Graphs. 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_4

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  • DOI: https://doi.org/10.1007/978-3-030-88361-4_4

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