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

Originally published on informit.com , 3 March 2012