How do I do a fuzzy match of company names in MYSQ

Question

My users will import through cut and paste a large string that will contain company names.

I have an existing and growing MYSQL database of companies names, each with a unique company_id.

I want to be able to parse through the string and assign to each of the user-inputed company names a fuzzy match.

Right now, just doing a straight-up string match, is also slow. ** Will Soundex indexing be faster? How can I give the user some options as they are typing? **

For example, someone writes:

Microsoft       -> Microsoft
Bare Essentials -> Bare Escentuals
Polycom, Inc.   -> Polycom

I have found the following threads that seem similar to this question, but the poster has not approved and I'm not sure if their use-case is applicable:

How to find best fuzzy match for a string in a large string database

Matching inexact company names in Java

Answer 1

May be its too late but it might help others. Check this link out.It uses the levenshtein distance metrics but is much faster. http://narenonit.blogspot.com/2012/07/fuzzy-matching-autocomplete-library.html

Answer 2

You can start with using SOUNDEX(), this will probably do for what you need (I picture an auto-suggestion box of already-existing alternatives for what the user is typing).

The drawbacks of SOUNDEX() are:

• its inability to differentiate longer strings. Only the first few characters are taken into account, longer strings that diverge at the end generate the same SOUNDEX value
• the fact the the first letter must be the same or you won't find a match easily. SQL Server has DIFFERENCE() function to tell you how much two SOUNDEX values are apart, but I think MySQL has nothing of that kind built in.
• for MySQL, at least according to the docs, SOUNDEX is broken for unicode input

Example:

SELECT SOUNDEX('Microsoft')
SELECT SOUNDEX('Microsift')
SELECT SOUNDEX('Microsift Corporation')
SELECT SOUNDEX('Microsift Subsidary')

/* all of these return 'M262' */

For more advanced needs, I think you need to look at the Levenshtein distance (also called "edit distance") of two strings and work with a threshold. This is the more complex (=slower) solution, but it allows for greater flexibility.

Main drawback is, that you need both strings to calculate the distance between them. With SOUNDEX you can store a pre-calculated SOUNDEX in your table and compare/sort/group/filter on that. With the Levenshtein distance, you might find that the difference between "Microsoft" and "Nzcrosoft" is only 2, but it will take a lot more time to come to that result.

In any case, an example Levenshtein distance function for MySQL can be found at codejanitor.com: Levenshtein Distance as a MySQL Stored Function (Feb. 10th, 2007).

Answer 3

This answer results in indexed lookup of almost any entity using input of 2 or 3 characters or more.

Basically, create a new table with 2 columns, word and key. Run a process on the original table containing the column to be fuzzy searched. This process will extract every individual word from the original column and write these words to the word table along with the original key. During this process, commonly occurring words like 'the','and', etc should be discarded.

We then create several indices on the word table, as follows...

• A normal, lowercase index on word + key
• An index on the 2nd through 5th character + key

• An index on the 3rd through 6th character + key

  Alternately, create a SOUNDEX() index on the word column.

Once this is in place, we take any user input and search using normal word = input or LIKE input%. We never do a LIKE %input as we are always looking for a match on any of the first 3 characters, which are all indexed.

If your original table is massive, you could partition the word table by chunks of the alphabet to ensure the user's input is being narrowed down to candidate rows immediately.

Answer 4

the best function for fuzzy matching is levenshtein. it's traditionally used by spell checkers, so that might be the way to go. there's a UDF for it available here: http://joshdrew.com/

the downside to using levenshtein is that it won't scale very well. a better idea might be to dump the whole table in to a spell checker custom dictionary file and do the suggestion from your application tier instead of the database tier.

Answer 5

SOUNDEX is an OK algorithm for this, but there have been recent advances on this topic. Another algorithm was created called the Metaphone, and it was later revised to a Double Metaphone algorithm. I have personally used the java apache commons implementation of double metaphone and it is customizable and accurate.

They have implementations in lots of other languages on the wikipedia page for it, too. This question has been answered, but should you find any of the identified problems with the SOUNDEX appearing in your application, it's nice to know there are options. Sometimes it can generate the same code for two really different words. Double metaphone was created to help take care of that problem.

Stolen from wikipedia: http://en.wikipedia.org/wiki/Soundex

As a response to deficiencies in the Soundex algorithm, Lawrence Philips developed the Metaphone algorithm for the same purpose. Philips later developed an improvement to Metaphone, which he called Double-Metaphone. Double-Metaphone includes a much larger encoding rule set than its predecessor, handles a subset of non-Latin characters, and returns a primary and a secondary encoding to account for different pronunciations of a single word in English.

At the bottom of the double metaphone page, they have the implementations of it for all kinds of programming languages: http://en.wikipedia.org/wiki/Double-Metaphone

Python & MySQL implementation: https://github.com/AtomBoy/double-metaphone

Answer 6

Firstly, I would like to add that you should be very careful when using any form of Phonetic/Fuzzy Matching Algorithm, as this kind of logic is exactly that, Fuzzy or to put it more simply; potentially inaccurate. Especially true when used for matching company names.

A good approach is to seek corroboration from other data, such as address information, postal codes, tel numbers, Geo Coordinates etc. This will help confirm the probability of your data being accurately matched.

There are a whole range of issues related to B2B Data Matching too many to be addressed here, I have written more about Company Name Matching in my blog, but in summary the key issues are:

• Looking at the whole string is unhelpful as the most important part of a Company Name is not necessarily at the beginning of the Company Name. i.e. ‘The Proctor and Gamble Company’ or ‘United States Federal Reserve ‘
• Abbreviations are common place in Company Names i.e. HP, GM, GE, P&G, D&B etc..
• Some companies deliberately spell their names incorrectly as part of their branding and to differentiate themselves from other companies.

Matching exact data is easy, but matching non-exact data can be much more time consuming and I would suggest that you should consider how you will be validating the non-exact matches to ensure these are of acceptable quality.

Before we built Match2Lists.com, we used to spend an unhealthy amount of time validating fuzzy matches. In Match2Lists we incorporated a powerful Visualisation tool enabling us to review non-exact matches, this proved to be a real game changer in terms of match validation, reducing our costs and enabling us to deliver results much more quickly.

Best of Luck!!