"Did you mean" feature on a dictionary database

Question

I have a ~300.000 row table; which includes technical terms; queried using PHP and MySQL + FULLTEXT indexes. But when I searching a wrong typed term; for example "hyperpext"; naturally giving no results.

I need to "compansate" little writing errors and getting nearest record from database. How I can accomplish such feaure? I know (actually, learned today) about Levenshtein distance, Soundex and Metaphone algorithms but currently not having a solid idea to implement this to querying against database.

Best regards. (Sorry about my poor English, I'm trying to do my best)

Answer 1

See this article for how you might implement Levenshtein distance in a MySQL stored function.

For posterity, the author's suggestion is to do this:

CREATE FUNCTION LEVENSHTEIN (s1 VARCHAR(255), s2 VARCHAR(255))
  RETURNS INT
    DETERMINISTIC
      BEGIN
        DECLARE s1_len, s2_len, i, j, c, c_temp, cost INT;
        DECLARE s1_char CHAR;
        DECLARE cv0, cv1 VARBINARY(256);
        SET s1_len = CHAR_LENGTH(s1), s2_len = CHAR_LENGTH(s2), cv1 = 0x00, j = 1, i = 1, c = 0;
        IF s1 = s2 THEN
          RETURN 0;
        ELSEIF s1_len = 0 THEN
          RETURN s2_len;
        ELSEIF s2_len = 0 THEN
          RETURN s1_len;
        ELSE
          WHILE j <= s2_len DO
            SET cv1 = CONCAT(cv1, UNHEX(HEX(j))), j = j + 1;
          END WHILE;
          WHILE i <= s1_len DO
            SET s1_char = SUBSTRING(s1, i, 1), c = i, cv0 = UNHEX(HEX(i)), j = 1;
            WHILE j <= s2_len DO
                SET c = c + 1;
                IF s1_char = SUBSTRING(s2, j, 1) THEN SET cost = 0; ELSE SET cost = 1; END IF;
                SET c_temp = CONV(HEX(SUBSTRING(cv1, j, 1)), 16, 10) + cost;
                IF c > c_temp THEN SET c = c_temp; END IF;
                SET c_temp = CONV(HEX(SUBSTRING(cv1, j+1, 1)), 16, 10) + 1;
                IF c > c_temp THEN SET c = c_temp; END IF;
                SET cv0 = CONCAT(cv0, UNHEX(HEX(c))), j = j + 1;
            END WHILE;
            SET cv1 = cv0, i = i + 1;
          END WHILE;
        END IF;
        RETURN c;
      END

He also supplies a LEVENSHTEIN_RATIO helper method which will evaluate the ratio of different/total characters, rather than a straight edit distance. For instance, if it's 60%, then three-fifths of the characters in the source word are different from the destination word.

CREATE FUNCTION LEVENSHTEIN_RATIO (s1 VARCHAR(255), s2 VARCHAR(255))
  RETURNS INT
    DETERMINISTIC
      BEGIN
        DECLARE s1_len, s2_len, max_len INT;
        SET s1_len = LENGTH(s1), s2_len = LENGTH(s2);
        IF s1_len > s2_len THEN SET max_len = s1_len; ELSE SET max_len = s2_len; END IF;
        RETURN ROUND((1 - LEVENSHTEIN(s1, s2) / max_len) * 100);
      END

Answer 2

From the comments of http://dev.mysql.com/doc/refman/5.0/en/udf-compiling.html

now i download the package from the mysql udf repository http://empyrean.lib.ndsu.nodak.edu/~nem/mysql/

wget http://empyrean.lib.ndsu.nodak.edu/~nem/mysql/udf/dludf.cgi?ckey=28

ll

tar -xzvf dludf.cgi\?ckey\=28

gcc -shared -o libmysqllevenshtein.so mysqllevenshtein.cc -I/usr/include/mysql/

mv libmysqllevenshtein.so /usr/lib

mysql -uroot -pPASS

mysql> use DATABASE

mysql> CREATE FUNCTION levenshtein RETURNS INT SONAME 'libmysqllevenshtein.so';

mysql> select levenshtein(w1.word,w2.word) as dist from word w1, word w2 where ETC........... order by dist asc limit  0,10;

Answer 3

Why not add a table column for storing the word in its alternate (e.g., Soundex) form? that way, if your first SELECT does not find the exact match, you can do a second search to look for matching alternate forms.

The trick is to encode each word so that misspelled variations end up converted into the same alternate form.

Answer 4

I suggest that you generate typo variations on the query input.

i.e. hyperpext > { hyperpeext, hipertext, ... } etc

One of these is bound to be the correct spelling (especially for common misspellings)

The way you identify the most likely match is to do a lookup for each on an index which tells you the document frequency of the term. (make sense?)