The need to do fuzzy string matching originally came about while developing the Gulf of Mexico Validator tool.
This article describes an in-house solution to the fuzzy string matching problem and its usefulness in solving a variety of problems which can allow us to automate tasks which previously required tedious user involvement. In many cases, it involves identifying words or phrases which are most similar to each other. The article is on a private site so I'll do my best to append the relevant contents here:įuzzy String Matching is the process of performing a human-like estimation of the similarity of two words or phrases. The implementation I came up with was relatively simple, and involved a weighted comparison of the length of the two phrases, the number of changes between each phrase, and whether each word could be found in the target entry. Part of the research involved implementing the Levenshtein distance algorithm, which determines how many changes must be made to a string or phrase to turn it into another string or phrase. The goal was to do some sort of fuzzy string search that could identify the database entry with the most common elements. I was presented with this problem about a year ago when it came to looking up user entered information about a oil rig in a database of miscellaneous information.
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