A data-driven approach for extracting "the most specific term" for ontology development.
We present a data-driven approach to extract the "most specific" terms relevant to an ontology of functioning, disability and health. The algorithm is a combination of statistical and linguistic approaches. The statistical filter is based on the frequency of the content words in a given text string; the linguistic heuristic is an extension of existing algorithms but goes beyond noun phrases and is formulated as a "complete syntactic node". Thus, it can be applied to any syntactic node of interest in the particular domain. Two test sets were marked by three experts. Test set 1 is a well-constructed text from pain abstracts; test set 2 is actual medical reports. Results are reported as recall, precision, F-score and rate of valid terms in false positives. A limitation of the current research is the relatively small test set.
Savova
Guergana K
GK
Division of Medical Informatics Research, Mayo Clinic, Rochester, MN, USA.
Harris
Marcelline
M
Johnson
Thomas
T
Pakhomov
Serguei V
SV
Chute
Christopher G
CG
D000465:Algorithms; D006262:Health; D008037:Linguistics; D009323:Natural Language Processing; D013223:Statistics as Topic; D018875:Vocabulary, Controlled