Fuzzification of Web Objects: A Semantic Web Mining Approach

Web Mining is becoming essential to support the web administrators and web users in multi-ways such as information retrieval; website performance management; web personalization; web marketing and website designing. Due to uncontrolled exponential growth in web data, knowledge base retrieval has become a very challenging task. The one viable solution to the problem is the merging of conventional web mining with semantic web technologies. This merging process will be more beneficial to web users by reducing the search space and by providing information that is more relevant. Key web objects play significant role in this process. The extraction of key web objects from a website is a challenging task. In this paper, we have proposed a framework, which extracts the key web objects from web log file and apply a semantic web to mine actionable intelligence. This proposed framework can be applied to non-semantic web for the extraction of key web objects. We also have defined an objective function to calculate key web object from user’s perspective. We named this function as key web object function. KWO function helps to fuzzify the extracted key web objects into three categories as Most Interested, Interested, and Least Interested. Fuzzification of web objects helps us to accommodate the uncertainty among the web objects of being user attractive. We also have validated the proposed scheme with the help of a case study.