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Area of Science:

  • Medical Informatics
  • Computational Linguistics

Background:

  • SNOMED CT is a compositional terminology enabling new meanings via post-coordinated expressions.
  • Implementing post-coordinated expressions in information systems necessitates specialized reasoner software for concept relation analysis.

Purpose of the Study:

  • To evaluate the performance of SNOMED CT reasoners when integrating a substantial volume of post-coordinated expressions.
  • To determine the impact of increasing post-coordinated expression numbers on ontology classification times.

Main Methods:

  • Measuring the time required for reasoners to classify an ontology comprising SNOMED CT and incrementally added post-coordinated expressions.
  • Assessing the scalability of reasoner performance with up to one million post-coordinated expressions.

Main Results:

  • The top-performing reasoner classified SNOMED CT with one million post-coordinated expressions in 42 seconds.
  • Ontology classification time exhibited a growth rate slightly less than quadratic as the dataset size increased.

Conclusions:

  • Current SNOMED CT reasoners demonstrate efficient performance even with extensive additions of post-coordinated expressions.
  • Classification time is not a limiting factor for implementing post-coordination in information systems with current SNOMED CT releases.