I have a fairly small corpus of structured records sitting in a database. Given a tiny fraction of the information contained in a single record, submitted via a web form (so structured in the same way as the table schema), (let us call it the test record) I need to quickly draw up a list of the records that are the most likely matches for the test record, as well as provide a confidence estimate of how closely the search terms match a record. The primary purpose of this search is to discover whether someone is attempting to input a record that is duplicate to one in the corpus. There is a reasonable chance that the test record will be a dupe, and a reasonable chance the test record will not be a dupe.
The records are about 12000 bytes wide and the total count of records is about 150,000. There are 110 columns in the table schema and 95% of searches will be on the top 5% most commonly searched columns.
The data is stuff like names, addresses, telephone numbers, and other industry specific numbers. In both the corpus and the test record it is entered by hand and is semistructured within an individual field. You might at first blush say "weight the columns by hand and match word tokens within them", but it's not so easy. I thought so too: if I get a telephone number I thought that would indicate a perfect match. The problem is that there isn't a single field in the form whose token frequency does not vary by orders of magnitude. A telephone number might appear 100 times in the corpus or 1 time in the corpus. The same goes for any other field. This makes weighting at the field level impractical. I need a more fine-grained approach to get decent matching.
My initial plan was to create a hash of hashes, top level being the fieldname. Then I would select all of the information from the corpus for a given field, attempt to clean up the data contained in it, and tokenize the sanitized data, hashing the tokens at the second level, with the tokens as keys and frequency as value.
I would use the frequency count as a weight: the higher the frequency of a token in the reference corpus, the less weight I attach to that token if it is found in the test record.
My first question is for the statisticians in the room: how would I use the frequency as a weight? Is there a precise mathematical relationship between n, the number of records, f(t), the frequency with which a token t appeared in the corpus, the probability o that a record is an original and not a duplicate, and the probability p that the test record is really a record x given the test and x contain the same t in the same field? How about the relationship for multiple token matches across multiple fields?
Since I sincerely doubt that there is, is there anything that gets me close but is better than a completely arbitrary hack full of magic factors?
Barring that, has anyone got a way to do this?
I'm especially keen on other suggestions that do not involve maintaining another table in the database, such as a token frequency lookup table :).
This is my first post on StackOverflow, thanks in advance for any replies you may see fit to give.
