Abstract
Web search engines are essential nowadays as if we, the end users want to know or gather certain information on any field is done by web. It’s a common tendency that people don’t prefer waiting for an information or searching all down many pages to obtain certain information. But as the information growth is tremendous the result set obtained by the search engines provide a burden of useless pages. It is even more irritable for the end users when pages with useless contents are ranked above. To resolve this, in this paper we have proposed a relation-based probability algorithm for page ranking for the semantic web search engines. To provide the necessary information to the end user the relevance of the pages must be obtained. Relevancy is measured as the probability that a retrieved resources actually contains the information needed by the user.
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Deisy, C., Rajeswari, A.M., Indra, R.M., Jayalakshmi, N., Mehalaa Devi, P.K. (2011). A Novel Relation-Based Probability Algorithm for Page Ranking in Semantic Web Search Engine. In: Dua, S., Sahni, S., Goyal, D.P. (eds) Information Intelligence, Systems, Technology and Management. ICISTM 2011. Communications in Computer and Information Science, vol 141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19423-8_15
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DOI: https://doi.org/10.1007/978-3-642-19423-8_15
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