Abstract:
Classification is one of the most widely
used data mining techniques with a lot of
extension. Query classification is crucial for web
search and advertising. Classification of web
queries is important to provide search result
effectively. As more and more documents become
electronically available, this system provides to
find documents in large database that fit users’
need. Information can be extracted from
summaries for the words contained in the
documents. This system presents the
implementation of query classification system for
information retrieval of conference papers in
digital library. In this system, Multinomial Naïve
Bayes algorithm is used in computing weights of
terms for each classes and then combined the
overall weights. Cosine similarity algorithm is
used to find the relevant documents. Resultant
documents are ranked according to their class
weights and similarity value. By using the system,
user can obtain more relevant result from digital
library since the document can be viewed by their
degree of similarity values.
