A ranking algorithm integrating vector space model with semantic metadata

Semantic Web is defined as a structured collection of information, which specifies the rules of inferences required to derive a specific piece of information from it. Data in semantic web is organized in a structured manner, and the relationships clearly defined amongst them. The potency of semantic web lies in is its capacity to comprehend the meaning of the given data, and connect it with other available pieces of information, to produce a new set of tangible knowledge. The algorithm developed in the paper takes advantage of this property of semantic web, and generates clusters of data based on the semantic metadata present in them. A system has been proposed to analyze the observations of algorithm implementation. The system is studied alongside an existing semantic web search engine, to assess the quality of its search results.