Using Similarity Metrics for Matching Lifelong Learners

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Access to resources and facilities Share information about pathways Reflect on current and future pathways

String metrics (also known as similarity metrics ) are a class of textual based metrics resulting in a similarity or dissimilarity ( distance ) score between two pairs of text strings for approximate matching or comparison and in fuzzy string searching . For example the strings "Sam" and "Samuel" can be considered (although not the same) to a degree similar. A string metric provides a floating point number indicating an algorithm-specific indication of similarity. The most widely known (although rudimentary) string metric is Levenshtein Distance (also known as Edit Distance), which operates between two input strings, returning a score equivalent to the number of transpositions , substitutions and deletions needed in order to transform one input string into another. Simplistic string metrics such as Levenshtein distance have expanded to include phonetic, token , grammatical and character-based methods of statistical comparisons .