Linking Biomedical Data to the Cloud

Stefan Zwicklbauer, Christin Seifert, Michael Granitzer

    Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

    2 Citations (Scopus)


    The application of Knowledge Discovery and Data Mining approaches forms the basis of realizing the vision of Smart Hospitals. For instance, the automated creation of high-quality knowledge bases from clinical reports is important to facilitate decision making processes for clinical doctors. A subtask of creating such structured knowledge is entity disambiguation that establishes links by identifying the correct semantic meaning from a set of candidate meanings to a text fragment. This paper provides a short, concise overview of entity disambiguation in the biomedical domain, with a focus on annotated corpora (e.g. CalbC), term disambiguation algorithms (e.g. abbreviation disambiguation) as well as gene and protein disambiguation algorithms (e.g. inter-species gene name disambiguation). Finally, we provide some open problems and future challenges that we expect future research will take into account.
    Original languageEnglish
    Title of host publicationSmart Health
    Subtitle of host publicationOpen Problems and Future Challenges
    EditorsAndreas Holzinger, Carsten Röcker, Martina Ziefle
    Number of pages27
    ISBN (Electronic)978-3-319-16226-3
    ISBN (Print)978-3-319-16225-6
    Publication statusPublished - 2015

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer International Publishing


    • Linked data cloud
    • Entity disambiguation
    • Text annotation
    • Natural language processing
    • Knowledge bases


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