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KnowWhereGraph-Lite: A Perspective of the KnowWhereGraph

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Knowledge Graphs and Semantic Web (KGSWC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14382))

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Abstract

KnowWhereGraph (KWG) is a massive, geo-enabled knowledge graph with a rich and expressive schema. KWG comes with many benefits including helping to capture detailed context of the data. However, the full KWG can be commensurately difficult to navigate and visualize for certain use cases, and its size can impact query performance and complexity. In this paper, we introduce a simplified framework for discussing and constructing perspectives of knowledge graphs or ontologies to, in turn, construct simpler versions; describe our exemplar KnowWhereGraph-Lite (KWG-Lite), which is a perspective of the KnowWhereGraph; and introduce an interface for navigating and visualizing entities within KWG-Lite called KnowWherePanel.

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Notes

  1. 1.

    https://knowwheregraph.org/.

  2. 2.

    The OWL ontology has over 300 classes and about 3,000 axioms.

  3. 3.

    https://support.google.com/knowledgepanel/answer/9163198?hl=en.

  4. 4.

    https://en.wikipedia.org/wiki/Infobox.

  5. 5.

    The general identification of which classes should feature prominently in the perspective is a human-centric process, which is outside of the scope of this paper.

  6. 6.

    https://github.com/KnowWhereGraph/knowwheregraph-lite/blob/main/construct-queries.md.

  7. 7.

    https://stko-kwg.geog.ucsb.edu/workbench/ and choosing KWG-Lite as the repository (top-right).

  8. 8.

    https://github.com/KnowWhereGraph/knowwheregraph-lite.

  9. 9.

    https://github.com/KnowWhereGraph/kw-panels.

  10. 10.

    https://knowwheregraph.org/kw-panels/.

  11. 11.

    https://creativecommons.org/licenses/by/4.0/.

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Acknowledgement

This work was funded by the National Science Foundation under Grant 2033521 A1: KnowWhereGraph: Enriching and Linking Cross-Domain Knowledge Graphs using Spatially-Explicit AI Technologies. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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Authors and Affiliations

  1. Wright State University, Dayton, USA

    Cogan Shimizu & Antrea Christou

  2. University of California, Santa Barbara, Santa Barbara, USA

    Shirly Stephen, Kitty Currier & Krzysztof Janowicz

  3. Kansas State University, Manhattan, USA

    Mohammad Saeid Mahdavinejad, Sanaz Saki Norouzi, Abhilekha Dalal, Adrita Barua & Pascal Hitzler

  4. Hydronos Labs, Princeton, USA

    Colby K. Fisher

  5. Michigan State University, East Lansing, USA

    Anthony D’Onofrio & Dean Rehberger

  6. Independent Researcher, Carmel, USA

    Thomas Thelen

  7. University of Vienna, Vienna, Austria

    Krzysztof Janowicz

  8. National Center for Ecological Analysis & Synthesis, Santa Barbara, USA

    Mark Schildhauer

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  1. Cogan Shimizu
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Corresponding author

Correspondence to Cogan Shimizu .

Editor information

Editors and Affiliations

  1. Autonomous University of Tamaulipas, Ciudad Victoria, Mexico

    Fernando Ortiz-Rodriguez

  2. University of La Rioja, Madrid, Spain

    Boris Villazón-Terrazas

  3. Autonomous University of Tamaulipas, Ciudad Victoria, Mexico

    Sanju Tiwari

  4. University of Zaragoza, Zaragoza, Spain

    Carlos Bobed

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Shimizu, C. et al. (2023). KnowWhereGraph-Lite: A Perspective of the KnowWhereGraph. In: Ortiz-Rodriguez, F., Villazón-Terrazas, B., Tiwari, S., Bobed, C. (eds) Knowledge Graphs and Semantic Web. KGSWC 2023. Lecture Notes in Computer Science, vol 14382. Springer, Cham. https://doi.org/10.1007/978-3-031-47745-4_15

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  • DOI: https://doi.org/10.1007/978-3-031-47745-4_15

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