| Abstract |
While redundant elements in SNOMED CT concept definitions are harmless
from a logical point of view, they unnecessarily make concept
definitions of typically large ontologies such as SNOMED CT hard to
construct and to maintain. In this paper, we apply a fully automated
method to detect intra-axiom redundancies in SNOMED CT. We
systematically analyse the completeness and soundness of the results of
our method by examining the identified redundant elements. In absence of
a gold standard, we check whether our method identifies concepts that
are likely to contain redundant elements because they become equivalent
to their stated subsumer when they are replaced by a fully defined
concept with the same definition. To evaluate soundness, we remove all
identified redundancies, and test whether the logical closure is
preserved by comparing the concept hierarchy to the one of the official
SNOMED CT distribution. We found that 35,010 of the 296,433 SNOMED CT
concepts (12%) contain redundant elements in their definitions, and that
the results of our method are sound and complete with respect to our
partial evaluation. We recommend to free the stated form from these
redundancies. In future, knowledge modellers should be supported by
being pointed to newly introduced redundancies.
This is a sneak preview of my presentation for AIME [2]
[1]
http://www.few.vu.nl/~kdr250/publications/AIME2013-Redundant-Elements-SNOMED.pdf
[2] http://www.aimedicine.info/aime13/index.html?v=1
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