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Abstract: Knowledge Organization Systems (KOS) play a key role in enriching biomedical information in order to make it machine-understandable and shareable. This is done by annotating medical documents, or more specifically, associating concept labels from KOS with pieces of digital information, e.g., images or texts. However, the dynamic nature of KOS may impact the annotations, thus creating a mismatch between the evolved concept and the associated information. To solve this problem, methods to maintain the quality of the annotations are required. In this paper, we define a framework based on rules, background knowledge and change patterns to drive the annotation adaption process. We evaluate experimentally the proposed approach in realistic cases-studies and demonstrate the overall performance of our approach in different KOS considering the precision, recall, F1-score and AUC value of the system.

Learning Objective 1: Semantic annotations commonly associated with clinical trials.

Authors:

Silvio Domingos Cardoso (Presenter)
Luxembourg Institute of Science and Technology

Chantal Reynaud-DelaƮtre, University of Paris-Sud XI
Marcos Da Silveira, Luxembourg Institute of Science and Technology
Cedric Pruski, Luxembourg Institute of Science and Technology

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