Support the extension of medical ontologies by automated reasoning

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About Support the extension of medical ontologies by automated reasoning

  • This project has not yet been fulfilled.
  • This project fits in the following Bachelor programs: {{#arraymap:Lifestyle Informatics|, |xXx|bachelorproject within::xXx|,}}
  • This project fits in the following masterareas: {{#arraymap:Human Ambience, Technical Artificial Intelligence, Knowledge Technology and Intelligent Internet Applications, Information Sciences|, |xXx|project within::xXx|,}}


Description

Medical ontologies such as SNOMED are very large (over 300.000 terms) and very sophisticated (using expressive description logics). Maintaining and updating such ontologies is a complicated task. In earlier research we have shown that over 10% of all definitions in SNOMED are logically redundant. To make matters worse, extensions to such ontologies are often required to capture particular medical specialties or national or regional practices. In this project you will investigate how automated reasoning techniques can be used to support the creation of such extensions, to ensure their quality, to avoid redundancy, and to maintain overall consistency.

Supervisors: Ronald Cornet (AMC) & Annette ten Teije