Probabilistic Semantic Web Mining Using Artificial Neural Analysis
  Most of the web user's requirements are search or navigation time and getting
correctly matched result. These constrains can be satisfied with some
additional modules attached to the existing search engines and web servers.
This paper proposes that powerful architecture for search engines with the
title of Probabilistic Semantic Web Mining named from the methods used. With
the increase of larger and larger collection of various data resources on the
World Wide Web (WWW), Web Mining has become one of the most important
requirements for the web users. Web servers will store various formats of data
including text, image, audio, video etc., but servers can not identify the
contents of the data. These search techniques can be improved by adding some
special techniques including semantic web mining and probabilistic analysis to
get more accurate results. Semantic web mining technique can provide meaningful
search of data resources by eliminating useless information with mining
process. In this technique web servers will maintain Meta information of each
and every data resources available in that particular web server. This will
help the search engine to retrieve information that is relevant to user given
input string. This paper proposing the idea of combing these two techniques
Semantic web mining and Probabilistic analysis for efficient and accurate
search results of web mining. SPF can be calculated by considering both
semantic accuracy and syntactic accuracy of data with the input string. This
will be the deciding factor for producing results.
