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KGTK: A Toolkit for Large Knowledge Graph Manipulation and Analysis

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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

Knowledge graphs (KGs) have become the preferred technology for representing, sharing and adding knowledge to modern AI applications. While KGs have become a mainstream technology, the RDF/SPARQL-centric toolset for operating with them at scale is heterogeneous, difficult to integrate and only covers a subset of the operations that are commonly needed in data science applications. In this paper we present KGTK, a data science-centric toolkit designed to represent, create, transform, enhance and analyze KGs. KGTK represents graphs in tables and leverages popular libraries developed for data science applications, enabling a wide audience of developers to easily construct knowledge graph pipelines for their applications. We illustrate the framework with real-world scenarios where we have used KGTK to integrate and manipulate large KGs, such as Wikidata, DBpedia and ConceptNet.

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Change history

  • 01 November 2020

    In the originally published version of chapter 18 the name of Rongpeng Li was misspelled. This has been corrected.

Notes

  1. 1.

    https://github.com/fhircat/CORD-19-on-FHIR/wiki/CORD-19-Semantic-Annotation-Projects.

  2. 2.

    https://neo4j.com.

  3. 3.

    https://graphy.link/.

  4. 4.

    https://rdflib.readthedocs.io/en/stable/.

  5. 5.

    https://graph-tool.skewed.de/.

  6. 6.

    https://networkx.github.io/.

  7. 7.

    https://spacy.io/.

  8. 8.

    https://linux.die.net/man/1/awk.

  9. 9.

    https://gephi.org/.

  10. 10.

    http://ctdbase.org/.

  11. 11.

    https://blender.cs.illinois.edu/.

  12. 12.

    A list of such projects can be found in https://github.com/fhircat/CORD-19-on-FHIR/wiki/CORD-19-Semantic-Annotation-Projects.

  13. 13.

    https://github.com/nasa-jpl-cord-19/covid19-knowledge-graph, https://github.com/GillesVandewiele/COVID-KG/.

  14. 14.

    http://pubannotation.org/collections/CORD-19.

  15. 15.

    https://scisight.apps.allenai.org/clusters.

  16. 16.

    https://github.com/vespa-engine/cord-19/blob/master/README.md.

  17. 17.

    https://kgtk.readthedocs.io/en/latest/specification/.

  18. 18.

    https://kgtk.readthedocs.io/en/latest.

  19. 19.

    https://linux.die.net/man/7/pipe.

  20. 20.

    https://github.com/usc-isi-i2/kgtk/tree/master/examples.

  21. 21.

    https://pandas.pydata.org.

  22. 22.

    https://dumps.wikimedia.org/wikidatawiki/entities/latest-truthy.nt.bz2.

  23. 23.

    https://github.com/usc-isi-i2/CKG-COVID-19/blob/dev/build-covid-kg.ipynb.

  24. 24.

    https://github.com/usc-isi-i2/kgtk/blob/master/examples/CSKG.ipynb.

  25. 25.

    https://datamart-upload.readthedocs.io/en/latest/REST-API-tutorial/.

  26. 26.

    https://github.com/NCATS-Tangerine/kgx.

  27. 27.

    https://tools.wmflabs.org/sqid/.

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Acknowledgements

This material is based on research sponsored by Air Force Research Laboratory under agreement number FA8750-20-2-10002. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of Air Force Research Laboratory or the U.S. Government.

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Authors and Affiliations

  1. Information Sciences Institute, University of Southern California, Los Angeles, USA

    Filip Ilievski, Daniel Garijo, Hans Chalupsky, Naren Teja Divvala, Yixiang Yao, Craig Rogers, Rongpeng Li, Jun Liu, Amandeep Singh & Pedro Szekely

  2. Department of Informatics, Pontificia Universidade Católica Rio de Janeiro, Rio de Janeiro, Brazil

    Daniel Schwabe

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  1. Filip Ilievski
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  2. Daniel Garijo
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  5. Yixiang Yao
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  6. Craig Rogers
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  7. Rongpeng Li
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Corresponding author

Correspondence to Filip Ilievski .

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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Ilievski, F. et al. (2020). KGTK: A Toolkit for Large Knowledge Graph Manipulation and Analysis. 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_18

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  • DOI: https://doi.org/10.1007/978-3-030-62466-8_18

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