@INPROCEEDINGS{DelPintoSchmidtGao22,
AUTHOR = {Del{-}Pinto, W. and Schmidt, R. A. and Gao, Y.},
YEAR = {2022},
TITLE = {Extracting Subontologies from {SNOMED} {CT}},
EDITOR = {Groth, P. and Rula, A. and Schneider, J. and Tiddi, I. and Simperl, E. and Alexopoulos, P. and Hoekstra, R. and Alam, M. and Dimou, A. and Tamper, M.},
BOOKTITLE = {The Semantic Web: {ESWC} 2022 Satellite Events},
SERIES = {Lecture Notes in Computer Science},
VOLUME = {13384},
PUBLISHER = {Springer},
PAGES = {291--294},
DOI = {10.1007/978-3-031-11609-4\_43},
URL = {http://www.cs.man.ac.uk/~schmidt/publications/DelPintoSchmidtGao22.html},
ABSTRACT = {
Computing smaller extracts of a larger ontology has been recognised as important
for enabling tasks such as ontology creation, review, updating, debugging,
navigation, sharing and integration. In addition, reasoning tasks such as
querying and classification take less time to execute over a smaller extract
than over the original ontology.
We have developed new software to compute concise extracts of SNOMED CT
that are semantically complete with respect to a set of input concepts,
called ocus concepts. The two main criteria for these extracts, called subontologies,
are: (i) Focus concepts must be defined equivalently in the subontology and
the source ontology. (ii) The transitive closure (with respect to subsumption)
between concepts occurring in the extract must be equal in the subontology and
the source ontology, up to the signature of concepts in the subontology.  The
subontology extraction approach automatically identifies additional supporting
concepts that are required to satisfy condition (i) and includes these in the
extracted subontology.
}
}