John discusses his program to add LC subject headings to titles in the Online Books Page based on the LC classification

From Glenn E. Patton:

"To get a sense of which MARC data elements are used in the various indexes for WorldCat, take a look at "Searching WorldCat Indexes" (http://www.oclc.org/support/documentation/worldcat/searching/searchworl
dcatindexes/). Click on "List of Fields and Subfields Indexed in Tag Order" in the navigation bar on the left of the screen to see a list of MARC elements and the indexes in which they are used."

 

 

"Ten things Google has found to be true".  Is there a message for libraries?  

Interesting post on federated searching. Why imporved mapping doesn't work, and what we might do to improve the results sets for users.

 

What Have We Got to Lose? The Effect of Controlled Vocabulary on Keyword Searching Results

Tina Gross and Arlene G.Taylor

Tips to keep in mind when searching Medline.

How do you find your way in an age of information overload? How can you filter streams of complex information to pull out only what you want? Why does it matter how information is structured when Google seems to magically bring up the right answer to your questions?

NC States project page -- the public page at least.

CQL::Parser is a Perl module for parsing Common Query Language statements.

CQL is a formal language for representing queries to information retrieval systems such as web indexes, bibliographic catalogs and museum collection information. The design objective is that queries be human readable and writable, and that the language be intuitive while maintaining the expressiveness of more complex languages.

CQL::Parser will allow you validate statements, and parse them into a parse tree which you can then programatically walk and use. For your convenince there are methods for converting the CQL parse tree into Swish and Lucene queries as well as XCQL (an XML representation of CQL).

  use CQL::Parser;
  my $parser = CQL::Parser->new();
  my $root = $parser->parse('dc.creator="clinton"');
  my $swish = $root->toSwish();
  my $lucene = $root->toLucene();
  my $xcql = $root->toXCQL();

Nice layout to look at for LoST

"To make this global panopticon a reality, unique identification tags are necessary but not sufficient. As we know from library science research, known-item searches account for less than half of total demand. Most users will also want to perform exploratory subject searches." woohoo!

Claims that the simplicity of google has

We present a new method for content management and knowledge discovery using a topology-preserving neural network. The method, termed topological organization of content (TOC), can generate a taxonomy of topics from a set of unannotated, unstructured documents. The TOC consists of a hierarchy of self-organizing growing chains (GCs), each of which can develop independently in terms of size and topics. The dynamic development process is validated continuously using a proposed entropy-based Bayesian information criterion (BIC). Each chain meeting the criterion spans child chains, with reduced vocabularies and increased specializations. This results in a topological tree hierarchy, which can be browsed like a table of contents directory or web portal. A brief review is given on existing methods for document clustering and organization, and clustering validation measures. The proposed approach has been tested and compared with several existing methods on real world web page datasets. The results have clearly demonstrated the advantages and efficiency in content organization of the proposed method in terms of computational cost and representation. The TOC can be easily adapted for large-scale applications. The topology provides a unique, additional feature for retrieving related topics and confining the search space.