Matching names to a database provides valuable information about people and things. When you match names, you use the person’s name or names of other items to determine the identity of the person or item. This post will explain the different name matching techniques companies and government organizations use.
Soundex method
The Soundex method is a phonetic algorithm that converts names into standard codes. The algorithm converts names into codes based on the sounds of the letters, with some exceptions. It is not case-sensitive and does not consider punctuation when converting names to codes.
Common key method
The common key method is a straightforward way to match names. To use this method, you’ll first collect data from multiple sources to generate a list of common names. Next, you’ll create a table showing the frequency of each name and its frequency count relative to the total number of individuals in your data set.
Finally, you’ll sort the data by name frequency by column and then by row until all names have been matched with their correct values. The main benefit of this method is that it’s relatively easy to implement and understand how it works.
List method
This is the most straightforward method of name matching. This method compares names with a list of names, i.e., known or common suffixes. In this method, a person’s name is compared against the names in the database for common suffixes or endings. The comparison can be made manually by going through each entry in your database, or it can be done automatically using scripts written to do so.
Edit distance method
The edit distance method is one of the most commonly used methods for matching names. It measures how similar two words are based on their spelling. The edit distance between two words can be calculated differently depending on how you want to define similarity. In this case, you will use Levenshtein edit distance, which calculates the minimum number of operations required to transform one string into another.
In addition to finding similar names, it can also find similar words or phrases to get more relevant results when searching for images online: `lions, tigers, bears` would give you pictures of lions, tigers and bears but not photos that contain all three animals together because those photographs have been matched with ones containing only one animal.
Word embedding method
Word embedding is a technique that maps words to vectors of real numbers. It has been used in natural language processing (NLP) for decades, and it helps computers understand the meaning of language by encoding the relationship between words.
In this method, you can represent each word as an n-dimensional vector where n is the number of dimensions you choose for your embedded space. The individual values in this vector correspond to how “close” they are related to each other. The closer two words are related, the closer their corresponding values will be in their embedding space.
Statistical similarity method
The statistical similarity method is based on the principle of how similar two words are. It is the most popular method and also the most accurate one. This method works by matching individual characters in a query word to find its potential matches within a set of documents (the “index”). Then these potential matches are checked for actual similarity using statistics. This approach can be used across languages and scripts, which makes it an ideal choice for multilingual applications.
Summary
This article has helped you understand the different ways of name matching and what they can do for you. It is a data-cleaning method that compares two or more lists to find matches, discrepancies, and other errors. This process can be used in any industry where data is collected, such as healthcare, insurance, education or government services.
