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SELECT Description FROM Production.ProductDescription WHERE CONTAINS(Description, 'FORMSOF (INFLECTIONAL, ride) ')
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This statement finds words that are inflectionally similar, such as verb conjugations and singular/plural forms of nouns. So words such as rode and riding-whip are found, but rodeo isn t. FREETEXT is similar to CONTAINS, but is much more liberal in finding variations. For example, a CONTAINS INFLECTIONAL search for two words would find that term and its inflections, whereas FREETEXT would find the inflections of two and words separately. Another aspect of fuzzy matches is using the thesaurus to find similar words. Curiously, the SQL Server thesaurus is empty when first installed. I populated the tsglobal.xml file (there are similar files for specific languages) with the following:
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<expansion> <sub>bicycle</sub> <sub>bike</sub> </expansion>
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Then I was able to query for any records containing bike or bicycle:
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SELECT Description FROM Production.ProductDescription WHERE CONTAINS(Description, 'FORMSOF (THESAURUS, bike) ')
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The thesaurus can also hold misspellings of words along with the proper spelling:
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<replacement> <pat>visualbasic</pat> <pat>vb</pat> <pat>visaul basic</pat> <sub>visual basic</sub> </replacement>
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If I were writing a resume-searching application, this could come in handy. The last option I want to cover here is the NEAR keyword, which looks for two words in close proximity to each other:
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SELECT Description FROM Production.ProductDescription WHERE CONTAINS(Description, 'bike NEAR woman')
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CONTAINS and FREETEXT have two cousins CONTAINSTABLE and FREETEXTTABLE. They return KEY and RANK information, which can be used for ranking your results:
SELECT [key], [rank] FROM CONTAINSTABLE(Production.ProductDescription, Description, 'bike') ORDER BY [rank] DESC
So far we ve covered the full range of text-searching features available in T-SQL, and we ve been able to perform many text-oriented queries. If it s impractical to build a thesaurus of misspellings and proper spellings, we have to use a more generic routine. Let s get to the core of this chapter and take a closer look at one answer to this problem.
Simil: an algorithm to look for similar strings
Simil
As shown earlier, T-SQL allows us to perform a wide range of text searches. Still, a lot remains to be desired, especially with regard to misspellings. If you want to find a set of records even if they have misspellings, or want to prevent misspellings, you need to perform fuzzy string comparisons, and Simil is one algorithm suited for that task. One use for Simil is in data cleanup. In one example, a company had a table with organic chemistry compounds, and their names were sometimes spelled differently. The application presents the user with the current record and similar records. The user can decide which records are duplicates, and choose the best one. One button click later, all child records are pointed to the chosen record, and the bad records are deleted. Then the user moves to the next record. Another typical use for Simil is in preventing bad data from entering the database in the first place. Our company has a Sales application with a Companies table. When a salesperson is creating or importing a new company, the application uses Simil to scan for similar company names. If it finds any records, it ll show a dialog box asking the user if the new company is one of those, or indeed a new company, as shown in figure 1. Other uses include educational software with open-ended questions. One tantalizing option the original authors mention is to combine Simil with a compiler, which could then auto-correct common mistakes. Let s look at Simil in more detail, and learn how we can take advantage of it. In 1988, Dr. Dobb s Journal published the Ratcliff/Obershelp algorithm for pattern recognition (Ratcliff and Metzener, Pattern Matching: The Gestalt Approach, http:/ / www.ddj.com/184407970 pgno=5). This algorithm compares two strings and returns a similarity between 0 (completely different) and 1 (identical). Ratcliff and Obershelp wrote the original version in assembly language for the 8086 processor. In 1999, Steve Grubb published his interpretation in the C language (http:/ /web.archive.org/web/
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