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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
Published on: February 23, 2019
A flexible measure of contextual similarity for biomedical terms
1Department of Chemistry, UMIST, Manchester, UK. i.spasic@umist.ac.uk
Abstract:
We present a measure of contextual similarity for biomedical terms. The contextual features need to be explored, because newly coined terms are not explicitly described and efficiently stored in biomedical ontologies and their inner features (e.g. morphologic or orthographic) do not always provide sufficient information about the properties of the underlying concepts. The context of each term can be represented as a sequence of syntactic elements annotated with biomedical information retrieved from an ontology. The sequences of contextual elements may be matched approximately by edit distance defined as the minimal cost incurred by the changes (including insertion, deletion and replacement) needed to transform one sequence into the other. Our approach augments the traditional concept of edit distance by elements of linguistic and biomedical knowledge, which together provide flexible selection of contextual features and their comparison.
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