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Table 10 Locality-based modules extracted from the SNOMED CT ontology for the 11 well-known medical conditions

From: Exploring semantic deep learning for building reliable and reusable one health knowledge from PubMed systematic reviews and veterinary clinical notes

Target term
(n-gram)
lower case
Number of SNOMED CT concept identifiers for the signatureTotal number of axiomsNumber of OWL ClassesNumber of OWL object propertiesNumber of SubClassOf axiomsNumber of EquivalentClass axioms
anaemia34105,20522,22746709215,134
arthritis3374,39816,1803174708709
asthma3797,64720,80439761113,192
ckd1951,89010,9294038997029
diabetes29463,437100,1194449,81850,300
epilepsy1910,07220852310551029
glaucoma39101,99721,2784411,04710,230
heart_failure3768,00614,6344454909143
hypertension31152,28332,2704816,42515,844
obesity2890,22418,9944012,3766617
osteoarthritis3873,49615,9033789226980
  1. The second column just reports the total number of SNOMED CT identifiers for the ontological signature. The worksheet “signatures” within the Additional file 3 contains the list of SNOMED CT identifiers (as signature) for each target term. From the third to the last column ontology metrics information for the locality-based module created per target term is provided. The last two columns indicate the number of descriptions and definitions extracted from the SNOMED CT ontology for each locality-based module, respectively