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Personalized Knowledge Graphs for the Pharmaceutical Domain

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The Semantic Web – ISWC 2019 (ISWC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11779))

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Abstract

A considerable amount of scientific and technical content is still locked behind data formats which are not machine readable, especially PDF files - and this is particularly true in the healthcare domain. While the Semantic Web has nourished the shift to more accessible formats, in business scenarios it is critical to be able to tap into this type of content, both to extract as well as embed machine readable semantic information.

We present our solution in the pharmaceutical domain and describe a fully functional pipeline to maintain up-to-date knowledge resources extracted from medication Package Inserts. We showcase how subject matter expert(s) can have their own view on the available documents, served by a personalized Knowledge Graph - or rather a view on the graph which is specific to them. We share lessons learned from our initial pilot study with a team of medical professionals. Our solution is fully integrated within the standard PDF data format and does not require the use of any external software - nor to be aware of the underlying graph.

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Notes

  1. 1.

    https://www.springernature.com/scigraph.

  2. 2.

    http://data.elsevier.com/documentation/index.html.

  3. 3.

    https://www.fda.gov/.

  4. 4.

    https://dailymed.nlm.nih.gov/dailymed.

  5. 5.

    https://bioportal.bioontology.org/.

  6. 6.

    As an example, if we consider “apple” as a seed term, Glimpse looks for all occurrences of “apple” in the underlying corpus and generates patterns using wildcards, such as “I like to eat * for breakfast” and “I invest in * stock”. Further details can be found in [3, 6].

  7. 7.

    We make sure that all selected drugs have at least 5 versions, obtained from DAILYMED.

  8. 8.

    https://pdfbox.apache.org/.

  9. 9.

    https://github.com/tesseract-ocr/tesseract.

  10. 10.

    https://www.mongodb.com/.

  11. 11.

    http://data.bioontology.org/documentation.

  12. 12.

    https://get.adobe.com/reader/.

  13. 13.

    Top 10 as for this use case, i.e. those 10 ontologies producing the bigger number of annotations in total.

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Authors and Affiliations

  1. IBM Research Almaden, San Jose, CA, USA

    Anna Lisa Gentile, Daniel Gruhl, Petar Ristoski & Steve Welch

Authors
  1. Anna Lisa Gentile
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  2. Daniel Gruhl
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  3. Petar Ristoski
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  4. Steve Welch
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Corresponding author

Correspondence to Anna Lisa Gentile .

Editor information

Editors and Affiliations

  1. Fondazione Bruno Kessler, Trento, Italy

    Chiara Ghidini

  2. Linköping University, Linköping, Sweden

    Olaf Hartig

  3. University of Bonn, Bonn, Germany

    Maria Maleshkova

  4. University of Economics Prague, Prague, Czech Republic

    Vojtěch Svátek

  5. University of Illinois at Chicago, Chicago, IL, USA

    Isabel Cruz

  6. University of Chile, Santiago, Chile

    Aidan Hogan

  7. Memect Technology, Beijing, China

    Jie Song

  8. Mines Saint-Etienne, Saint-Etienne, France

    Maxime Lefrançois

  9. Inria Sophia Antipolis - Méditerranée, Sophia Antipolis, France

    Fabien Gandon

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Gentile, A.L., Gruhl, D., Ristoski, P., Welch, S. (2019). Personalized Knowledge Graphs for the Pharmaceutical Domain. In: Ghidini, C., et al. The Semantic Web – ISWC 2019. ISWC 2019. Lecture Notes in Computer Science(), vol 11779. Springer, Cham. https://doi.org/10.1007/978-3-030-30796-7_25

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  • DOI: https://doi.org/10.1007/978-3-030-30796-7_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30795-0

  • Online ISBN: 978-3-030-30796-7

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