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.
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