A fuzzy search is a method of locating web pages that return highly relevant results even if the search terms used do not necessarily match the desired information. Websites that have the exact matches appear on top of the list, but other relevant results will also be included on the next pages.

A fuzzy search is best for specific kinds of searches, as it can also reveal results that may otherwise be filtered out by search algorithms that apply hard-and-fast rules. Using a fuzzy matching program can address search terms that contain typographical errors. The program returns results for alternative spellings and homophones.

A fuzzy search is like consulting an online encyclopedia or a thesaurus with a cross-referencing feature. As such, users can get related search terms that can serve as valuable resources.

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What Are the Commonly Used Fuzzy Search Name Matching Techniques?

A fuzzy search relies heavily on the effectiveness of a fuzzy matching program. Several name matching methods are ideal for a particular kind of search. But while there may be different ways of fuzzy matching, the most effective are those that use multiple methods to return results. Here are some of the commonly used fuzzy search name matching techniques: 

1. Common Key Method

The common key method translates names to a key, depending on their English pronunciation. All names that sound the same would have a similar key. The technique employs phonetic algorithms to convert similar-sounding names to one code. Some of the popular algorithms include Soundex, Metaphone, and Double Metaphone. While this method is easy to implement, it is not precise and needs manual inspection.

2. List Method

The list method tries to enumerate all possible spelling variations of each name before looking for matches. A simple name can have thousands of transliterations, requiring organizations to invest in expensive hardware to accommodate intensive searches. Additionally, it cannot process unrecognized names and those that have additional or missing spaces.

3. Edit Distance Method

The edit distance method uses the number of character changes needed to change a name to another. For example, Kate and Cate will have an edit distance of 1 because it requires only one transposition from “K” to “C.” But the names Catharine and Katherine would have an edit distance of 2 because the “C” should be “K,” and the second “a” should be “e.” Despite being a quick method of matching, it fails to capture linguistic nuances, as each edit is given the same weight. 

4. Statistical Similarity Method

The statistical similarity fuzzy search method is the most used, as it can take thousands of name pairs and train a model to identify similar names and assign a similarity score accordingly. The method provides high accuracy and can directly match names even when they come in different languages.

5. Word Embedding Method

The word embedding method is commonly applied for fuzzy searches for an organization’s name. Organization names differ from human names because they may contain synonyms that sound different from the target name.

Since many fuzzy search name matching methods have flaws, a hybrid approach is often recommended.