Adaptive and Optimization of Personalized Information Retrieval Model in Semantic Web
J. I. Christy Eunaicy1, S. Suguna2
1J. I. Christy Eunaicy, Research Scholar, Bharathiar University, Coimbatore (Tamil Nadu), India.
2Dr. S. Suguna, Assistant Professor, Department of Computer Science, Sri Meenakshi Govt college for Women, Madurai (Tamil Nadu), India.
Manuscript received on 12 October 2019 | Revised Manuscript received on 21 October 2019 | Manuscript Published on 02 November 2019 | PP: 808-814 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B11310982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1131.0982S1119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The recognition of user’s visited set of web pages for the prediction of web page is a key drawback. Thus the work is employed with the web access log files which is stored in the server. To understand the user interest patterns, the web access log files are extracted that depicts the user behavior. Various applications can be employed to predicting user’s behavior while serving the web. During this work, the proposed framework analyze the user usage, reinforced the content and the content retrieved with the semantic manner. The semantic information retrieval supported the user access pages are preprocessed and the web log data of the particular user is analyzed to identify the user profile. Then the retrieved information is graded with clustering the semantic content based results. The ranked content is then analyzed with the user profile to produce an optimized search results for the users based on the user classification.
Keywords: Semantic Web, Pattern Extraction, Web Content Mining, Web Usage Mining, Semantic Cluster.
Scope of the Article: Web and Text Mining