How do I do a fuzzy match of company names in MYSQL with PHP for auto-complete?
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
Here's a link to the php discussion of the soundex functions in mysql and php. I'd start from there, then expand into your other not-so-well-defined requirements.
Your reference references the Levenshtein methodology for matching. Two problems. 1. It's more appropriate for measuring the difference between two known words, not for searching. 2. It discusses a solution designed more to detect things like proofing errors (using "Levenshtien" for "Levenshtein") rather than spelling errors (where the user doesn't know how to spell, say "Levenshtein" and types in "Levinstein". I usually associate it with looking for a phrase in a book rather than a key value in a database.
EDIT: In response to comment--
- Can you at least get the users to put the company names into multiple text boxes; 2. or use an unambigous name delimiter (say backslash); 3. leave out articles ("The") and generic abbreviations (or you can filter for these); 4. Squoosh the spaces out and match for that also (so Micro Soft => microsoft, Bare Essentials => bareessentials); 5. Filter out punctuation; 6. Do "OR" searches on words ("bare" OR "essentials") - people will inevitably leave one or the other out sometimes.
Test like mad and use the feedback loop from users.
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).
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 (also an updated article), 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!!