Time-Oriented Question Answering from Clinical Narratives Using Semantic-Web Techniques

  • Cui Tao
  • Harold R. Solbrig
  • Deepak K. Sharma
  • Wei-Qi Wei
  • Guergana K. Savova
  • Christopher G. Chute
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6497)


The ability to answer temporal-oriented questions based on clinical narratives is essential to clinical research. The temporal dimension in medical data analysis enables clinical researches on many areas, such as, disease progress, individualized treatment, and decision support. The Semantic Web provides a suitable environment to represent the temporal dimension of the clinical data and reason about them. In this paper, we introduce a Semantic-Web based framework, which provides an API for querying temporal information from clinical narratives. The framework is centered by an OWL ontology called CNTRO (Clinical Narrative Temporal Relation Ontology), and contains three major components: time normalizer, SWRL based reasoner, and OWL-DL based reasoner. We also discuss how we adopted these three components in the clinical domain, their limitations, as well as extensions that we found necessary or desirable to archive the purposes of querying time-oriented data from real-world clinical narratives.


Temporal Information Temporal Relation Time Stamp Sample Query Basic Formal Ontology 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Cui Tao
    • 1
  • Harold R. Solbrig
    • 1
  • Deepak K. Sharma
    • 1
  • Wei-Qi Wei
    • 1
  • Guergana K. Savova
    • 2
  • Christopher G. Chute
    • 1
  1. 1.Division of Biomedical Statistics and InformaticsMayo ClinicRochester
  2. 2.Harvard Medical SchoolBoston

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