Hello,
I'm using Lucene 1.9 to replace an in-house search engine where all of the
documents to be searched are also created in-house. One of the features of the
search engine is something called 'xtras' which are associated with the
documents. I am wondering how best to model this feature using Lucene. I have
one solution (offered below for critique) but I'm not sure it's the best way,
being a Lucene newbie.
First let me better explain 'xtras' and how they work in the *old* search
engine. A document can have zero or more 'xtras'. 'xtras' consist of a token
and a weight. At index time this weight is taken into account when computing
a score which is saved in the index.
The index is a database table with three columns and PK of (token, docid):
token => document id => score
The search algorithm is pretty obvious from here. A user enters in a query,
it's parsed into tokens, and we gather up all the unique document ids and add
their scores together. In SQL the logic is something like this:
SELECT docid, SUM(score) AS score
FROM SearchIndex
WHERE token IN (...constructed from user query...)
GROUP BY docid
ORDER BY score DESC
The 'xtras' come into play when saving the score to the index. Each row in the
index is a triple: (token, docid, score). The base score is calculated somehow
and then the 'xtra' weight is merely tacked on to the final value saved.
For example, here is a document with an id of 'foo' and two 'xtras':
Document:
id: foo
xtra:
token: breed
weight: 2
xtra:
token: dog
weight: 10
When this document gets indexed the tokens 'breed' and 'dog' will have some
base score calculated some how. This base score could be 0 if the token isn't
even in the document. Then the weight is added onto this base score and the
results saved to the index. So assume 'breed' has a base score of 1.2 and
'dog' has a base score of 0.4 then the rows saved to the index are:
(breed, foo, 3.2)
(dog, foo, 10.4)
There are some 12,000 in-house created documents that I am searching and nearly
all of them have these associated 'xtras'. I feel like this is a huge hint to
any search engine and that it should be taken advantage of. The information is
already there and new documents are created every day with these little hints.
In more popular terminology 'xtras' are kind of like tags with weights.
So, I want to use Lucene as the basis for a new search engine and I want to
take this already out there information into account. I have developed one
approach which works okay, no complaints or problems really, but I feel like
it's wrong some how. My solution is as follows:
I noticed that 99.9% of the 'xtras' had weights less than 10. So in my Lucene
index I create 11 fields:
xtra_1, xtra_2, xtra_3, ..., xtra_10, xtra_max
In field 'xtra_1' I stick all of the tokens (joined by spaces) which have a
weight of 1, in field 'xtra_2' I stick all the tokens that have a weight of 2,
and so on. In 'xtra_max' I stick all the tokens with a weight of more than 10.
I give field xtra_1 a boost of 20, field xtra_2 a boost of 40, and so, with
field xtra_10 a boost of 200. Field xtra_max gets a gigantic boost of 10000.
I picked the scaling value of 20 for the first 10 fields out of thin air, same
with the boost for xtra_max.
I'm a QueryParser fan, so that's what I've been using. Our current search
language is very primitive so QueryParser is a huge bonus and probably good
enough for us. However, now that I've created all these new fields I need to
search them all. So, obviously, MultiFieldQueryParser is what I moved to.
When I search a document I have 13 fields that get passed to
MultiFieldQueryParser. 'body', 'title', and the 11 'xtra' fields above. So
far this has worked well enough. I can clearly see that the 'xtras' and their
weights influence the final rankings.
In all honestly, I don't have any complaints quite yet. However, I am left
with a feeling that the above is kind of "dirty" and that there is a better
way. For example, had the values of the 'xtras' ranged more wildly I don't
think my approach would've scaled. Also, it feels like this should be a
common problem and perhaps I just lack the vocabulary to find the right
approach.
So, is what I am doing problematic or is it an okay approach? Am I going to
run into some kind of wall eventually? Is there some library or API methods I
missed which do exactly what I want which I somehow blindly missed (if so,
sorry!)?
Thanks for any input!
-matthew
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