Home > Articles > Open Source > Python

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.
Like this article? We recommend

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.

A common way to solve the string-search problem is to look for values that are "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 it isn't 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's 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 by 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:

#!/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 sounds are encoded with the same hash value, but the results are not ideal:

$ 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

In this example, the variations Theresa and Teresa both produce the same Soundex hash, but Catherine and Katherine start with a different letter; 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.

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 (NYSIIS) algorithm 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.

#!/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 our sample data:

$ 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

In this case, 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.

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.

#!/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 didn't, 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.

Standalone 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 don't 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're 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's still used in many cases because it's built into the database server. Its simplicity also means that it's 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 that this article has demonstrated 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 you're performing. If the right algorithm isn't clear from the data available, it may be best to provide an option to let users select an appropriate hash algorithm. Offering the user a choice will provide the 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 their implementations, so presenting them as options shouldn't intimidate these users.

References

InformIT Promotional Mailings & Special Offers

I would like to receive exclusive offers and hear about products from InformIT and its family of brands. I can unsubscribe at any time.

Overview


Pearson Education, Inc., 221 River Street, Hoboken, New Jersey 07030, (Pearson) presents this site to provide information about products and services that can be purchased through this site.

This privacy notice provides an overview of our commitment to privacy and describes how we collect, protect, use and share personal information collected through this site. Please note that other Pearson websites and online products and services have their own separate privacy policies.

Collection and Use of Information


To conduct business and deliver products and services, Pearson collects and uses personal information in several ways in connection with this site, including:

Questions and Inquiries

For inquiries and questions, we collect the inquiry or question, together with name, contact details (email address, phone number and mailing address) and any other additional information voluntarily submitted to us through a Contact Us form or an email. We use this information to address the inquiry and respond to the question.

Online Store

For orders and purchases placed through our online store on this site, we collect order details, name, institution name and address (if applicable), email address, phone number, shipping and billing addresses, credit/debit card information, shipping options and any instructions. We use this information to complete transactions, fulfill orders, communicate with individuals placing orders or visiting the online store, and for related purposes.

Surveys

Pearson may offer opportunities to provide feedback or participate in surveys, including surveys evaluating Pearson products, services or sites. Participation is voluntary. Pearson collects information requested in the survey questions and uses the information to evaluate, support, maintain and improve products, services or sites, develop new products and services, conduct educational research and for other purposes specified in the survey.

Contests and Drawings

Occasionally, we may sponsor a contest or drawing. Participation is optional. Pearson collects name, contact information and other information specified on the entry form for the contest or drawing to conduct the contest or drawing. Pearson may collect additional personal information from the winners of a contest or drawing in order to award the prize and for tax reporting purposes, as required by law.

Newsletters

If you have elected to receive email newsletters or promotional mailings and special offers but want to unsubscribe, simply email information@informit.com.

Service Announcements

On rare occasions it is necessary to send out a strictly service related announcement. For instance, if our service is temporarily suspended for maintenance we might send users an email. Generally, users may not opt-out of these communications, though they can deactivate their account information. However, these communications are not promotional in nature.

Customer Service

We communicate with users on a regular basis to provide requested services and in regard to issues relating to their account we reply via email or phone in accordance with the users' wishes when a user submits their information through our Contact Us form.

Other Collection and Use of Information


Application and System Logs

Pearson automatically collects log data to help ensure the delivery, availability and security of this site. Log data may include technical information about how a user or visitor connected to this site, such as browser type, type of computer/device, operating system, internet service provider and IP address. We use this information for support purposes and to monitor the health of the site, identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents and appropriately scale computing resources.

Web Analytics

Pearson may use third party web trend analytical services, including Google Analytics, to collect visitor information, such as IP addresses, browser types, referring pages, pages visited and time spent on a particular site. While these analytical services collect and report information on an anonymous basis, they may use cookies to gather web trend information. The information gathered may enable Pearson (but not the third party web trend services) to link information with application and system log data. Pearson uses this information for system administration and to identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents, appropriately scale computing resources and otherwise support and deliver this site and its services.

Cookies and Related Technologies

This site uses cookies and similar technologies to personalize content, measure traffic patterns, control security, track use and access of information on this site, and provide interest-based messages and advertising. Users can manage and block the use of cookies through their browser. Disabling or blocking certain cookies may limit the functionality of this site.

Do Not Track

This site currently does not respond to Do Not Track signals.

Security


Pearson uses appropriate physical, administrative and technical security measures to protect personal information from unauthorized access, use and disclosure.

Children


This site is not directed to children under the age of 13.

Marketing


Pearson may send or direct marketing communications to users, provided that

  • Pearson will not use personal information collected or processed as a K-12 school service provider for the purpose of directed or targeted advertising.
  • Such marketing is consistent with applicable law and Pearson's legal obligations.
  • Pearson will not knowingly direct or send marketing communications to an individual who has expressed a preference not to receive marketing.
  • Where required by applicable law, express or implied consent to marketing exists and has not been withdrawn.

Pearson may provide personal information to a third party service provider on a restricted basis to provide marketing solely on behalf of Pearson or an affiliate or customer for whom Pearson is a service provider. Marketing preferences may be changed at any time.

Correcting/Updating Personal Information


If a user's personally identifiable information changes (such as your postal address or email address), we provide a way to correct or update that user's personal data provided to us. This can be done on the Account page. If a user no longer desires our service and desires to delete his or her account, please contact us at customer-service@informit.com and we will process the deletion of a user's account.

Choice/Opt-out


Users can always make an informed choice as to whether they should proceed with certain services offered by InformIT. If you choose to remove yourself from our mailing list(s) simply visit the following page and uncheck any communication you no longer want to receive: www.informit.com/u.aspx.

Sale of Personal Information


Pearson does not rent or sell personal information in exchange for any payment of money.

While Pearson does not sell personal information, as defined in Nevada law, Nevada residents may email a request for no sale of their personal information to NevadaDesignatedRequest@pearson.com.

Supplemental Privacy Statement for California Residents


California residents should read our Supplemental privacy statement for California residents in conjunction with this Privacy Notice. The Supplemental privacy statement for California residents explains Pearson's commitment to comply with California law and applies to personal information of California residents collected in connection with this site and the Services.

Sharing and Disclosure


Pearson may disclose personal information, as follows:

  • As required by law.
  • With the consent of the individual (or their parent, if the individual is a minor)
  • In response to a subpoena, court order or legal process, to the extent permitted or required by law
  • To protect the security and safety of individuals, data, assets and systems, consistent with applicable law
  • In connection the sale, joint venture or other transfer of some or all of its company or assets, subject to the provisions of this Privacy Notice
  • To investigate or address actual or suspected fraud or other illegal activities
  • To exercise its legal rights, including enforcement of the Terms of Use for this site or another contract
  • To affiliated Pearson companies and other companies and organizations who perform work for Pearson and are obligated to protect the privacy of personal information consistent with this Privacy Notice
  • To a school, organization, company or government agency, where Pearson collects or processes the personal information in a school setting or on behalf of such organization, company or government agency.

Links


This web site contains links to other sites. Please be aware that we are not responsible for the privacy practices of such other sites. We encourage our users to be aware when they leave our site and to read the privacy statements of each and every web site that collects Personal Information. This privacy statement applies solely to information collected by this web site.

Requests and Contact


Please contact us about this Privacy Notice or if you have any requests or questions relating to the privacy of your personal information.

Changes to this Privacy Notice


We may revise this Privacy Notice through an updated posting. We will identify the effective date of the revision in the posting. Often, updates are made to provide greater clarity or to comply with changes in regulatory requirements. If the updates involve material changes to the collection, protection, use or disclosure of Personal Information, Pearson will provide notice of the change through a conspicuous notice on this site or other appropriate way. Continued use of the site after the effective date of a posted revision evidences acceptance. Please contact us if you have questions or concerns about the Privacy Notice or any objection to any revisions.

Last Update: November 17, 2020