[topicmapmail] Generation of Topic Maps and Machine Learning

[Lars Marius Garshol]

Hi Aayush,

* Aayush Puri
| 
| I wanted to know then possibilities one has in order to generate
| topic maps from a given source of textual documents. So what I will
| have is a text source and what I am interesting in doing is to
| generate topic maps between "certain" topics. For simplicity the
| topics among which I need to draw associations are limited and are
| pre-defined (at the time of providing the textual sources).

I think you are right that for this you do need NLP, really. 
 
| I think this would certainly require some NLP etc. but that too is
| new for me. 

Steve Pepper wrote a paper on a project he and I did that might be of
use to you:

  <URL: http://www.ontopia.net/topicmaps/materials/xmlconf.html >

I've got some papers on concept extraction that may be interesting to
you:

  <URL: http://elj.warwick.ac.uk/jilt/99-1/osborn.html >
  <URL: http://www.law.kuleuven.ac.be/icri/publications/435moens.pdf >
  <URL: http://baron.pagemewhen.com:8080/ace/ace_ramirez_mattmann.pdf >

This one is particularly interesting, since it also has association
extraction covered:

  <URL: http://xtasy.slis.indiana.edu/biosifter/papers/fubio.pdf >

| Also what are the possibilities that some machine learning algorithm
| can be applied so that the system betters the associations (and
| hence the quality of topic maps) whenever provided with more sample
| sets (in this case more textual information related to the topics).

I don't know of any NLP methods that work in this way. There are
methods that do classification (working out what a document is about)
and learn as they go along. However, classification is much easier
than concept and association extraction, and I don't know of anything
that can apply learning to this task, nor can I really imagine how it
would work.

-- 
Lars Marius Garshol, Ontopian         <URL: http://www.ontopia.net >
GSM: +47 98 21 55 50                  <URL: http://www.garshol.priv.no >