{"id":21,"date":"2019-10-25T07:28:41","date_gmt":"2019-10-25T07:28:41","guid":{"rendered":"https:\/\/arms10.org\/publication\/?p=21"},"modified":"2020-01-31T06:13:28","modified_gmt":"2020-01-31T06:13:28","slug":"ontology-based-web-usage-mining-model","status":"publish","type":"post","link":"https:\/\/arms10.org\/publication\/ontology-based-web-usage-mining-model\/","title":{"rendered":"Ontology Based Web Usage Mining Model"},"content":{"rendered":"\n
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Due to unprecedented growth of information on the Web and lack of structure in many Web sites, it became real challenge to the Web users to find relevant information. To solve this problem, Personalization is considered as a popular solution to customize the World Wide Web environment toward the user\u2019s preferences. Recent study shows that Web Usage Mining techniques play an important role in designing Web page recommendation systems. However the present conventional content-based recommender systems, built using the Web Usage Mining process, do not take Semantic Knowledge into pattern discovery and recommendation process. Recent studies show that integrating domain knowledge in the form of ontology into Web Usage Mining process can enhance the quality of the discovered usage patterns. Our work aims to incorporate semantics knowledge in all the phases of Web Usage mining process. CloSpan, a state-of-the-art algorithm for Sequential Pattern mining is applied over the Semantic space to generate frequent Sequential Patterns. The generated semantically enriched patterns are fed to Web page Recommendation model in offline phase. Experimental results shown are promising and showed a significant improvement on the quality of the recommendations. <\/p>\n\n\n\n

\n\nWith the explosive growth of information on World Wide\nWeb, it has become a real challenge for Web users to find and\naccess relevant information. One possible approach to address\nthis challenge is personalizing the user\u2019s Web experience [1].\nWeb Personalization [2] is the process of customizing a Web\nsite to meet the needs of each specific user or set of users, by\ntaking advantage of the knowledge acquired through the\nanalysis of the user\u2019s navigational behavior.\nWeb Recommender system is a specialized personalization\nsystem. Understanding the information needs of users has\nbecome a crucial task for Web site owners in improving, the\nservices offered by them and to retain the customers. A key\nrequirement in developing successful personalized web\napplications is to build user models that can accurately\nrepresent user\u2019s interests and preferences. Further the user\nmodels should have the ability to encode the information in\nmachine-understandable format for addressing semantic\nheterogeneity issues.\nThere are various ways to model user needs. Web Usage\nMining techniques are considered to be popular in modeling\nuser\u2019s web browsing behavior and thus patterns discovered\nfrom mining process are used in various applications such as\nbuilding recommendation systems. Web Usage Mining is a\nprocess of extracting interesting and frequent navigational\npatterns by applying Data mining techniques on Web log files.\nHowever conventional Web usage based recommender\nsystems are limited in their ability to use the domain\nknowledge of the Web application and their focus is only on\nWeb usage data. As a consequence, the quality of the\ndiscovered patterns is low. These patterns do not provide\nexplicit insight into the user\u2019s underlying interests and\npreferences, thus limiting the effectiveness of recommendation\nsystem in interpreting and justifying the recommendations [3].\nRecent studies [4] claim that Ontology, which represents\nthe domain knowledge of the application in Semantic form, if\nintegrated with Web Usage Mining can enhance the quality of\ngenerated usage patterns and help in building effective\nRecommendation systems. The combination of Web Usage\nMining and Semantic Web has created a new and fast\nemerging research theme \u2013 Semantic Web Usage Mining [5].\nThe key contributions of our work are summarized as\nfollows:\n1) Building a model to formalize the user\u2019s Web browsing\nactivities in Semantic form.\n2) Feeding domain Ontology into Web Usage Mining\nProcess for extracting Sequential navigational patterns. Such\nintegration allows more pruning of the search space in\nSequential pattern mining of the web log file.\n3) A state-of-the-art algorithm is used in the Sequential\nPattern mining process to generate frequent Sequential\nPatterns and subsequently sequential association rules are\ngenerated.\n4) Generated Sequential rules have antecedent and\nconsequent as sequence of ontological instances instead of\nmere page views.\n The rest of the paper is organized as follows. In section\nII we review recent advances in the area of Semantic Web\nUsage Mining. In section III proposed model and architecture\nare discussed. Experimental set up and performance\nevaluation of the proposed model is presented in section IV.\nFinally section V provides the concluding remarks and sheds\nlight on possible future enhancements. \n\n<\/p>\n","protected":false},"excerpt":{"rendered":"

Due to unprecedented growth of information on the Web and lack of structure in many Web sites, it became real challenge to the Web users to find relevant information. 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