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A Knowledge Graph for Assessing Agressive Tax Planning Strategies

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The Semantic Web – ISWC 2020 (ISWC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12507))

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Abstract

The taxation of multi-national companies is a complex field, since it is influenced by the legislation of several states. Laws in different states may have unforeseen interaction effects, which can be exploited by allowing multinational companies to minimize taxes, a concept known as tax planning. In this paper, we present a knowledge graph of multinational companies and their relationships, comprising almost 1.5 M business entities. We show that commonly known tax planning strategies can be formulated as subgraph queries to that graph, which allows for identifying companies using certain strategies. Moreover, we demonstrate that we can identify anomalies in the graph which hint at potential tax planning strategies, and we show how to enhance those analyses by incorporating information from Wikidata using federated queries.

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Notes

  1. 1.

    https://www.gleif.org/en/.

  2. 2.

    https://data.worldbank.org/.

  3. 3.

    https://www.oecd.org/tax/tax-policy/corporate-tax-statistics-database.htm.

  4. 4.

    https://www.wikidata.org/wiki/Property:P297.

  5. 5.

    https://www.wikidata.org/wiki/Property:P1278.

  6. 6.

    https://www.wikidata.org/wiki/Q64.

  7. 7.

    The full code for generating the knowledge graph is available online at https://github.com/tax-graph/taxgraph.

  8. 8.

    http://taxgraph.informatik.uni-mannheim.de/.

  9. 9.

    https://en.wikipedia.org/wiki/Rabbit-duck_illusion.

  10. 10.

    https://thecorrespondent.com/6942.

  11. 11.

    See, e.g., https://www.offshorecompany.com/company/denmark-holding/.

  12. 12.

    The top 10 lists, however, have to be taken with a grain of salt. For a city to appear in the top 10 list, it requires that (a) we are able to link it to Wikidata using the approach sketched in Sect. 2, and (b) it has to have its area as a value in Wikidata. Therefore, those lists cannot be considered complete.

  13. 13.

    https://www.ffiec.gov/NPW.

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Author information

Authors and Affiliations

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

    Niklas Lüdemann, Ageda Shiba, Nikolaos Thymianis, Nicolas Heist & Heiko Paulheim

  2. Area Accounting and Taxation, University of Mannheim, Mannheim, Germany

    Christopher Ludwig

  3. Corporate Taxation and Public Finance Department, Leibniz Centre for European Economic Research, Mannheim, Germany

    Christopher Ludwig

Authors
  1. Niklas Lüdemann
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  2. Ageda Shiba
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  3. Nikolaos Thymianis
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  4. Nicolas Heist
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  5. Christopher Ludwig
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  6. Heiko Paulheim
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Corresponding author

Correspondence to Heiko Paulheim .

Editor information

Editors and Affiliations

  1. University of Edinburgh, Edinburgh, UK

    Jeff Z. Pan

  2. University of Liverpool, Liverpool, UK

    Valentina Tamma

  3. University of Bari, Bari, Italy

    Claudia d’Amato

  4. University of California, Santa Barbara, Santa Barbara, CA, USA

    Krzysztof Janowicz

  5. California State University, Long Beach, Long Beach, CA, USA

    Bo Fu

  6. Vienna University of Economics and Business, Vienna, Austria

    Axel Polleres

  7. Rensselaer Polytechnic Institute, Troy, NY, USA

    Oshani Seneviratne

  8. Massachusetts Institute of Technology, Cambridge, MA, USA

    Lalana Kagal

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Lüdemann, N., Shiba, A., Thymianis, N., Heist, N., Ludwig, C., Paulheim, H. (2020). A Knowledge Graph for Assessing Agressive Tax Planning Strategies. In: Pan, J.Z., et al. The Semantic Web – ISWC 2020. ISWC 2020. Lecture Notes in Computer Science(), vol 12507. Springer, Cham. https://doi.org/10.1007/978-3-030-62466-8_25

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  • DOI: https://doi.org/10.1007/978-3-030-62466-8_25

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