Explosive growth of the Internet makes it difficult for search engines to give relevant results to the users within a stipulated time. Search engines store the web pages in classified directories and for this process even though some search engines depend on human expertise; most of the search engines use automated methods for classification of web pages. In this paper we use machine-learning techniques for the automated classification of web pages. We consider only URL features for classification as the URL name is unique, meaningful and helps identification of their subject categories most of the times. Experimental results show that machine learning techniques for automated classification of web pages with URL features proves to be the best and more useful method for search engines.
Semantic web is the technology which drives the syntactic search and there are a wide variety of applications available for tourism sector today which promotes the country's economic status. This paper concerned with the development of a model towards the semantic search and the result which is based on user's priority while searching the tourism domain of interest. From this proposed model, the conditional probability for the given input can be calculated and querying ontology to provide relevant information. This proposed model has been developed with use of Netica-J. The ontology is being created with Protégé which is the tool used as an ontology editor and the SPARQL is used for querying the ontology. The interface between the ontology and SPARQL is being made with the help of Jena.
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