
## Probabilistic Semantic Web Mining Using Artificial Neural Analysis 1. T. Krishna Kishore, 2. T.Sasi Vardhan, And 3. N. Lakshmi Narayana Abstract

 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. 

## 1. Concept Of Data Mining 1.1.1 Data

Data are any facts, numbers, or text that can be processed by a computer. Today, organizations are accumulating vast and growing amounts of data in different formats and different databases. This includes: 
 Operational or transactional data such as, sales, cost, inventory, payroll, and accounting No operational data, such as industry sales, forecast data, and macro economic data Meta data - data about the data itself, such as logical database design or data dictionary definitions 

## 1.1.2 Information

The patterns, associations, or relationships among all this *data* can provide *information*. For example, analysis of retail point of sale transaction data can yield information on which products are selling and when. 

## 1.1.3 Knowledge

Information can be converted into knowledge about historical patterns and future trends. For example, summary information on retail supermarket sales can be analyzed in light of promotional efforts to provide knowledge of consumer buying behavior. Thus, a manufacturer or retailer could determine which items are most susceptible to promotional efforts. 

## 1.1.4 Data Warehouses

Dramatic advances in data capture, processing power, data transmission, and storage capabilities are enabling organizations to integrate their various databases into *data warehouses*. Data warehousing is defined as a process of centralized 