3

Right now I am using ts_rank (with ts_vector and ts_query) to search strings for relevance. While this is doing a great job for me so far. I was hoping to work in a bit of fuzzieness into the searching.

As it stands the search is done by a user entering their terms as a string, that I then parse into tokens. These tokens are then compared against a string. What I would like to do is use something similar to a levenshtein in these queries to allow for minor misspellings to be matched as well.

For example if the user typed in rubico instead of rubicon I would still get a match on it (because rubico only has a distance of 1 from rubicon).

Is this possible to do with Postgres' ts_rank functionality, or are there other options to allow for text searches to work with levenstheins?

1

Instead of matching the portion of the string that I wanted to fuzzy search on I instead, concatinated all of the terms into a string, and then ran the difference function (from the fuzzystrmatch module) against it. So it looked something like this.

FOR term IN terms LOOP
    term_string := term_string||' '||term;
END LOOP;

score := score + difference(term_string,string_to_compare_against);

I did need to set a threshold that needed to be passed by the score to keep from getting to many false positives.

1

It can be done, but it's not necessarily fun, or fast.

Your best bet for fuzzy matching is "Soft TFIDF" (pdf), probably using Jaro Winkler similarity. Jaro Winkler is similar to Levenshtein but weights letters more heavily at the beginning of a string. It's available in the pg_similarity extension.

Here's a function I wrote a while ago, which I haven't tested rigorously. It's basically a port from here https://github.com/TeamCohen/secondstring

array_unique() is from the smlar extension. tokenize() just splits on non-alphanums. The table smlar_stats is like this:

create table smlar_stats (
  value text unique,
  ndoc int not null
);

You'll probably want to create such a table dynamically though.

CREATE OR REPLACE FUNCTION soft_tfidf(s text, t text, debug boolean DEFAULT false)
  RETURNS real AS
$BODY$
declare
    s_bag text[] = array_unique( tokenize(s) );
    t_bag text[] = array_unique( tokenize(t) );
    tok text;
    tok_j text;
    sim real = 0.0;
    jw real;
    threshold real = 0.78;
    match_score real;
    match_tok text;
begin

    if debug then raise notice 's_bag: % t_bag: %', s_bag, t_bag; end if;

    foreach tok in array s_bag loop
        if inarray(t_bag, tok) then 
            sim = sim + bag_weight(s_bag, tok) * bag_weight(t_bag, tok);
        else 
            match_score = threshold;
            match_tok = null;
            foreach tok_j in array t_bag loop
                jw = jarowinkler(tok, tok_j);  
                if jw >= match_score then
                    match_tok = tok_j;
                    match_score = jw;   if debug then raise notice 'jw(%,%)=%', tok, tok_j, jw; end if;
                end if;
            end loop;
            if match_tok is not null then
                sim = sim + bag_weight(s_bag, tok) * bag_weight(t_bag, match_tok) * match_score;
            end if;
        end if;
    end loop;

    return sim;

end $BODY$
  LANGUAGE plpgsql VOLATILE STRICT


CREATE OR REPLACE FUNCTION bag_weight(text[], text)
  RETURNS real AS
$BODY$
declare
    w real;
    normalizer real = 0.0;
    token text;
    df real;
    weights real[] = '{}';
begin

    foreach token in array $1 loop
        select ndoc from smlar_stats where value = token into df;
        if df is null then df = 1.0; end if;
        w = log(2) * log(301.1 / df);
        weights = array_append(weights,  w);
        normalizer = normalizer + w*w;
    end loop;

    normalizer = sqrt(normalizer);

    for i in 1..array_length($1, 1) loop
        if $1[i] = $2 then
            return weights[i] / normalizer;
        end if;
    end loop;

end $BODY$
  LANGUAGE plpgsql VOLATILE STRICT
  • how do we populate smlar_stats table? – zlatko Apr 6 '17 at 13:55

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