SCOPAS — SEMANTIC COMPUTATION OF PAGE SCORE

Abstract

This paper presents a novel model for scoring web pages, entitled SCOPAS (Semantic COmputation of PAge Score). With the prolific growth in the number of users of World Wide Web and the heterogeneity of their information needs, it becomes mandatory to evaluate the relevance of a web page in terms of user specific requirements. SCOPAS is aimed at modeling the web pages to facilitate efficient evaluation by harnessing the inherent features of the page in terms of its content and structure. The proposed model further enriches the scoring procedure by fine-graining the evaluation to a micro level through segmentation of the page. A variable magnitude, multi-dimensional approach is proposed for evaluating each of the segments by incorporating the relevance of intra-segment level components. The user-interest is captured with the help of FOAF (Friend Of A Friend) Ontology to achieve personalized page scoring. The generic SCOPAS model is extended to SCOPAS-Rank, which explores utilization of the model in improving the web search engine's result ordering. A prototype implementation of the proposed SCOPAS-Rank model is developed and experiments were conducted on it. The results of the experiments validate the effectiveness of the proposed model.