Hierarchical Fuzzy Clustering Semantics (HFCS) in Web Document for Discovering Latent Semantics

This paper discusses about the future of the World Wide Web development, called Semantic Web. Undoubtedly, Web service is one of the most important services on the Internet, which has had the greatest impact on the generalization of the Internet in human societies. Internet penetration has been an effective factor in growth of the volume of information on the Web. The massive growth of information on the Web has led to some problems, the most important one is search query. Nowadays, search engines use different techniques to deliver high quality results, but we still see that search results are not ideal. It should also be noted that information retrieval techniques to a certain extent can increase the search accuracy. Most of the web content is designed for human usage and machines are only able to understand and manipulate data at word level. This is the major limitation for providing better services to web users. The solution provided for this topic is to display the content of the web in such a way that it can be readily understood and comprehensible to the machine. This solution, which will lead to a huge transformation on the Web is called the Semantic Web and will begin. Better results for responding to the search for semantic web users, is the purpose of this research. In the proposed method, the expression, searched by the user, will be examined according to the related topics. The response obtained from this section enters to a rating system, which is consisted of a fuzzy decision-making system and a hierarchical clustering system, to return better results to the user. It should be noted that the proposed method does not require any prior knowledge for clustering the data. In addition, accuracy and comprehensiveness of the response are measured. Finally, the F test is applied to obtain a criterion for evaluating the performance of the algorithm and systems. The results of the test show that the method presented in this paper can provide a more precise and comprehensive response than its similar methods and it increases the accuracy up to 1.22%, on average.