Abstract

We describe a method for automating the detection and correction of spelling errors in the Foundational Model of Anatomy (FMA). The FMA was tokenized into 4893 distinct words; misspellings were identified and corrected using the National Library of Medicine’s SPECIALIST GSpell Spelling Suggestion API. We identified 43 errors occurring in 97 terms, and 6 words of questionable or inconsistent spelling occurring in 26 terms. These errors are replicated in other reference terminologies that use the FMA. Our approach may be useful as part of a quality assurance process for other large-scale biomedical knowledge resources.

Publication Type:	Conference or Workshop Item (Paper)
Subjects:	R Medicine > RA Public aspects of medicine Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources
Departments:	School of Mathematics, Computer Science & Engineering > Computer Science
URI:	https://openaccess.city.ac.uk/id/eprint/1039

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