Re: String similarity search vs. typcial IR application...

[Leo Galambos]

Exact matches are not ideal for DNA applications, I guess. I am not a 
DNA expert, but those guys often need a feature that is termed 
``fuzzy''[*] in Lucene. They need Levenstein's and Hamming's metrics, 
and I think that Lucene has many drawbacks which disallow effective 
implementations. On the other hand, I am very interested in a method you 
mentioned. Could you give me a reference, please? Thank you.

-g-

[*] why do you use the label ``fuzzy''? It has nothing to do with fuzzy 
logic or fuzzy IR, I guess.

Frank Burough wrote:

I have seen some interesting work done on storing DNA sequence as a set of common patterns with unique sequence between them. If one uses an analyzer to break sequence into its set of patterns and unique sequence then Lucene could be used to search for exact pattern matches. I know of only one sequence search tool that was based on this approach. I don't know if it ever left the lab and made it into the mainstream. If I have time I will explore this a bit.

Frank Burough



 

-----Original Message-----
From: Leo Galambos [mailto:[EMAIL PROTECTED] 
Sent: Thursday, June 05, 2003 5:55 PM
To: Lucene Users List
Subject: Re: String similarity search vs. typcial IR application...

AFAIK Lucene is not able to look DNA strings up effectively. 
You would 
use DASG+Lev (see my previous post - 05/30/2003 1916CEST).

-g-

Jim Hargrave wrote:

   

Our application is a string similarity searcher where the 
     

query is an 
   

input string and we want to find all "fuzzy" variants of the 
     

input string in the DB.  The Score is basically dice's 
coefficient: 2C/Q+D, where C is the number of terms (n-grams) 
in common, Q is the number of unique query terms and D is the 
number of unique document terms. Our documents will be sentences.
   

I know Lucene has a fuzzy search capability - but I assume 
     

this would 
   

be very slow since it must search through the entire term 
     

list to find candidates.
   

In order to do the calculation I will need to have 'C' - the 
     

number of 
   

terms in common between query and document. Is there an API 
     

that I can call to get this info? Any hints on what it will 
take to modify Lucene to handle these kinds of queries?
   



     

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