Linking Biomedical Data to the Cloud

Stefan Zwicklbauer, Christin Seifert, Michael Granitzer

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

2 Citations (Scopus)
4 Downloads (Pure)


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
Place of PublicationCham
Number of pages27
ISBN (Electronic)978-3-319-16226-3
ISBN (Print)978-3-319-16225-6
Publication statusPublished - 2015
Externally publishedYes

Publication series

NameLecture Notes in Computer Science
PublisherSpringer International Publishing
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • Linked data cloud
  • Entity disambiguation
  • Text annotation
  • Natural language processing
  • Knowledge bases
  • n/a OA procedure


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