HSWS

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Abstract
With the tremendous growth of information availability to users through the Web, search engines come to play ever a more critical role. However, search engines retrieve vast amount of information that is far larger than an individual capability of processing. Also enormous amount of such information are not related to user search query. The semantic web provides a promising approach to enhance the search operation. Ontologies can capture concepts for any topic to enable machines to deal with data semantically. In this paper, a proposed technique called HSWS (Hybrid Semantic Web Search) is used to automatically generate ontology concepts for any topic by extracting semantic relationships between concepts from information sources that represent such topic; the resultant ontology concepts is used to re-rank results returned by a search engine for such topic. A new relevancy measure is proposed to rank retrieved documents. The new relevancy measure depends on the degree of semantic similarity between concepts extracted from web pages resulted from query on a topic and indexed ontology concepts that represents the topic. The proposed technique is fully unsupervised; it doesn't require any type of training. HSWS outperforms semantic similarity state-of-the-art methods and web-based methods. The proposed technique shows the highest Pearson correlation coefficient to human judgments (0.888) compared to other similarity methods.

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