  Search engines perform the task of retrieving information related to the
user-supplied query words. This task has two parts; one is finding "featured
words" which describe an article best and the other is finding a match among
these words to user-defined search terms. There are two main independent
approaches to achieve this task. The first one, using the concepts of
semantics, has been implemented partially. For more details see another paper
of Marko et al., 2002. The second approach is reported in this paper. It is a
theoretical model based on using Neural Network (NN). Instead of using keywords
or reading from the first few lines from papers/articles, the present model
gives emphasis on extracting "featured words" from an article. Obviously we
propose to exclude prepositions, articles and so on, that is, English words
like "of, the, are, so, therefore, " etc. from such a list. A neural model is
taken with its nodes pre-assigned energies. Whenever a match is found with
featured words and userdefined search words, the node is fired and jumps to a
higher energy. This firing continues until the model attains a steady energy
level and total energy is now calculated. Clearly, higher match will generate
higher energy; so on the basis of total energy, a ranking is done to the
article indicating degree of relevance to the user's interest. Another
important feature of the proposed model is incorporating a semantic module to
refine the search words; like finding association among search words, etc. In
this manner, information retrieval can be improved markedly.
