Using Fuzzy Matching to Search by Sound with Python
When you’re writing code to search a database, you can’t rely on all those data entries being spelled correctly. Doug Hellmann, developer at DreamHost and author of The Python Standard Library By Example, reviews available options for searching databases by the sound of the target’s name, rather than relying on the entry’s accuracy.
Searching for a person’s name in a database is a unique challenge. Depending on the source and age of the data, you may not be able to count on the spelling of the name being correct, or even the same name being spelled the same way when it appears more than once. Discrepancies between stored data and search terms may be introduced due to personal choice or cultural differences in spellings, homophones, transcription errors, illiteracy, or simply lack of standardized spellings during some time periods. These sorts of problems are especially prevalent in transcriptions of handwritten historical records used by historians, genealogists, and other researchers.
Discrepancies between stored data and search terms may be introduced due to personal choice or cultural differences in spellings, homophones, transcription errors, illiteracy, or simply lack of standardized spellings during some time periods.
A common way to solve the string search problem is to look for values “close” to the same as the search target. Using a traditional fuzzy match algorithm to compute the closeness of two arbitrary strings is expensive, though, and is not appropriate for searching large data sets. A better solution is to compute hash values for entries in the database in advance, and several special hash algorithms have been created for this purpose. These phonetic hash algorithms allow you to compare two words or names based on how they sound, rather than the precise spelling.
Early Efforts: Soundex
One such algorithm is Soundex, developed by Margaret K. Odell and Robert C. Russell in the early 1900s. The Soundex algorithm appears frequently in genealogical contexts because it is associated with the U.S. Census and is specifically designed to encode names. A Soundex hash value is calculated by using the first letter of the name and converting the consonants in the rest of the name to digits using a simple lookup table. Vowels and duplicate encoded values are dropped, and the result is padded up to–or truncated down to–four characters. The Fuzzy library includes a Soundex implementation for Python programs.
The Soundex algorithm appears frequently in genealogical contexts because it is associated with the U.S. Census and is specifically designed to encode names.
#!/usr/bin/env python
import fuzzy
names = [ 'Catherine', 'Katherine', 'Katarina',
'Johnathan', 'Jonathan', 'John',
'Teresa', 'Theresa',
'Smith', 'Smyth',
'Jessica',
'Joshua',
]
soundex = fuzzy.Soundex(4)
for n in names:
print '%-10s' % n, soundex(n)
The output of show_soundex.py demonstrates that some of the names with similar sound are encoded with the same hash value, but the results are not ideal. The variations “Theresa” and “Teresa” both produce the same Soundex hash, but “Catherine” and “Katherine” start with a different letter so even though they sound the same the hash outputs are different. The last two names, “Jessica” and “Joshua,” are not related at all but are given the same hash value because the letters “J,” “S,” and “C” all map to the digit 2, and the algorithm removes duplicates. These types of failures illustrate a major shortcoming of Soundex.
$ python show_soundex.py
Catherine C365
Katherine K365
Katarina K365
Johnathan J535
Jonathan J535
John J500
Teresa T620
Theresa T620
Smith S530
Smyth S530
Jessica J200
Joshua J200
Beyond English: NYSIIS
Algorithms developed after Soundex use different encoding schemes, either building on Soundex by tweaking the lookup table or starting from scratch with their own rules. All of them process phonemes differently in an attempt to improve accuracy. For example, in the 1970s the New York State Identification and Intelligence System algorithm (NYSIIS) was published by Robert L. Taft. NYSIIS was originally used by what is now the New York State Division of Criminal Justice Services to help identify people in their database. It produces better results than Soundex because it takes special care to handle phonemes that occur in European and Hispanic surnames.
NYSIIS produces better results than Soundex because it takes special care to handle phonemes that occur in European and Hispanic surnames.
#!/usr/bin/env python
import fuzzy
names = [ 'Catherine', 'Katherine', 'Katarina',
'Johnathan', 'Jonathan', 'John',
'Teresa', 'Theresa',
'Smith', 'Smyth',
'Jessica',
'Joshua',
]
for n in names:
print '%-10s' % n, fuzzy.nysiis(n)
The output of show_nysiis.py is better than the results from Soundex with this sample data because “Catherine”, “Katherine”, and “Katarina” all map to the same hash value. The incorrect match of “Jessica” and “Joshua” is also eliminated because more of the letters from the names are used in the NYSIIS hash values.
$ python show_nysiis.py
Catherine CATARAN
Katherine CATARAN
Katarina CATARAN
Johnathan JANATAN
Jonathan JANATAN
John JAN
Teresa TARAS
Theresa TARAS
Smith SNATH
Smyth SNATH
Jessica JASAC
Joshua JAS
A New Approach: Metaphone
Metaphone, published in 1990 by Lawrence Philips, is another algorithm that improves on earlier systems such as Soundex and NYSIIS. The Metaphone algorithm is significantly more complicated than the others because it includes special rules for handling spelling inconsistencies and for looking at combinations of consonants in addition to some vowels. An updated version of the algorithm called Double Metaphone goes even further by adding rules for handling some spellings and pronunciations from languages other than English.
The Metaphone algorithm is significantly more complicated than the others because it includes special rules for handling spelling inconsistencies and for looking at combinations of consonants in addition to some vowels.
#!/usr/bin/env python
import fuzzy
names = [ 'Catherine', 'Katherine', 'Katarina',
'Johnathan', 'Jonathan', 'John',
'Teresa', 'Theresa',
'Smith', 'Smyth',
'Jessica',
'Joshua',
]
dmetaphone = fuzzy.DMetaphone(4)
for n in names:
print '%-10s' % n, dmetaphone(n)
In addition to having a broader set of encoding rules, Double Metaphone generates two alternate hashes for each input word. This gives the caller the ability to present search results with two levels of precision. In the results from the sample program, “Catherine” and “Katherine” have the same primary hash value. Their secondary hash value is the same as the primary hash for “Katarina,” finding the match that Soundex did not, but giving it less weight than the results from NYSIIS implied.
$ python show_dmetaphone.py
Catherine ['K0RN', 'KTRN']
Katherine ['K0RN', 'KTRN']
Katarina ['KTRN', None]
Johnathan ['JN0N', 'ANTN']
Jonathan ['JN0N', 'ANTN']
John ['JN', 'AN']
Teresa ['TRS', None]
Theresa ['0RS', 'TRS']
Smith ['SM0', 'XMT']
Smyth ['SM0', 'XMT']
Jessica ['JSK', 'ASK']
Joshua ['JX', 'AX']
Applying Phonetic Searches
Using phonetic searches in your application is straightforward, but may require adding extensions to the database server or bundling a third-party library with your application. MySQL, Postgresql, SQLite, and Microsoft SQL Server all support Soundex through a string function that can be invoked directly in queries. Postgresql also includes functions to calculate hashes using the original Metaphone algorithm and Double Metaphone.
Using phonetic searches in your application is straightforward, but may require adding extensions to the database server or bundling a third-party library with your application.
Stand-alone implementations for all of the algorithms also are available for major programming languages such as Python, PHP, Ruby, Perl, C/C++, and Java. These libraries can be used with databases that do not have support for phonetic hash functions built in, such as MongoDB. For example, this script loads a series of names into a database, saving each hash value as a precomputed value to make searching easier later.
#!/usr/bin/env python
import argparse
import fuzzy
from pymongo import Connection
parser = argparse.ArgumentParser(description='Load names into the database')
parser.add_argument('name', nargs='+')
args = parser.parse_args()
c = Connection()
db = c.phonetic_search
dmetaphone = fuzzy.DMetaphone()
soundex = fuzzy.Soundex(4)
for n in args.name:
# Compute the hashes. Save soundex
# and nysiis as lists to be consistent
# with dmetaphone return type.
values = {'_id':n,
'name':n,
'soundex':[soundex(n)],
'nysiis':[fuzzy.nysiis(n)],
'dmetaphone':dmetaphone(n),
}
print 'Loading %s: %s, %s, %s' %
(n, values['soundex'][0], values['nysiis'][0],
values['dmetaphone'])
db.people.update({'_id':n}, values,
True, # insert if not found
False,
)
Run mongodb_load.py from the command line to save names for retrieval later.
$ python mongodb_load.py Jonathan Johnathan Joshua Jessica
Loading Jonathan: J535, JANATAN, ['JN0N', 'ANTN']
Loading Johnathan: J535, JANATAN, ['JN0N', 'ANTN']
Loading Joshua: J200, JAS, ['JX', 'AX']
Loading Jessica: J200, JASAC, ['JSK', 'ASK']
$ python mongodb_load.py Catherine Katherine Katarina
Loading Catherine: C365, CATARAN, ['K0RN', 'KTRN']
Loading Katherine: K365, CATARAN, ['K0RN', 'KTRN']
Loading Katarina: K365, CATARAN, ['KTRN', None]
The search program mongodb_search.py lets the user select a hash function and then constructs a MongoDB query to find all names with a hash value matching the input name.
#!/usr/bin/env python
import argparse
import fuzzy
from pymongo import Connection
ENCODERS = {
'soundex':fuzzy.Soundex(4),
'nysiis':fuzzy.nysiis,
'dmetaphone':fuzzy.DMetaphone(),
}
parser = argparse.ArgumentParser(description='Search for a name in the database')
parser.add_argument('algorithm', choices=('soundex', 'nysiis', 'dmetaphone'))
parser.add_argument('name')
args = parser.parse_args()
c = Connection()
db = c.phonetic_search
encoded_name = ENCODERS[args.algorithm](args.name)
query = {args.algorithm:encoded_name}
for person in db.people.find(query):
print person['name']
In some of these sample cases the extra values in the result set are desirable because they are valid matches. On the other hand, the Soundex search for “Joshua” returns the unrelated value “Jessica” again. Although Soundex produces poor results when compared to the other algorithms, it is still used in many cases because it is built in to the database server. Its simplicity also means it is faster than the NYSIIS or Double Metaphone. In situations where the results are good enough, its speed may be a deciding factor in selecting it.
$ python mongodb_search.py soundex Katherine
Katherine
Katarina
$ python mongodb_search.py nysiis Katherine
Catherine
Katherine
Katarina
$ python mongodb_search.py soundex Joshua
Joshua
Jessica
$ python mongodb_search.py nysiis Joshua
Joshua
Final Thoughts
I hope this article demonstrates the power that phonetic hash algorithms can add to the search features of your application, and the ease with which you can implement them. Selecting the right algorithm to use will depend on the nature of the data and the types of searches being performed. If the right algorithm is not clear from the data available, it may be best to provide an option to the user to let them pick a hash algorithm. Offering the user a choice will provide most flexibility for experimentation and refining searches, although it does require a little more work on your part to set up the indexes. Many researchers, historians, and genealogists are familiar with the names of the algorithms, if not the implementations, so presenting them as options should not intimidate users.
References
- “The Soundex Indexing System”
- Taft, R. L. (1970), “Name Search Techniques”, Albany, New York: New York State Identification and Intelligence System
- Philips, Lawrence, “The Double Metaphone Search Algorithm”
- Fuzzy
Originally published on informit.com , 3 March 2012