Improving Precision In Ranking Semantic Associations

Abstract

Accessing relevant information from the Web has become difficult newlinedue to the explosive growth of information on the Web Many users try to newlineacquire this information by using search engine but search engine based newlinesystems locate only documents based on the keywords or key phrases While newlineconsidering data on the Web different entities can be related in multiple ways newlinethat cannot be predefined But in the Semantic Web the RDF data model newlinecaptures the meaning of an entity by specifying its relationship with other newlineentities newlineAt present many applications such as intelligence analysis newlinegenetics pharmaceutical research and flight security require more complex newlinerelationships than simple direct relationships between entities Semantic newlineAssociation is a sequence of complex relationships between entities in a newlineknowledge base represented as a graph Searching semantic relationships newlineamong the entities like people places and events from the semantic web is an newlineessential component in the future While searching semantic associations in newlineRDF graph the result containing multiple paths connecting two entities is newlineperceived Each path has different meanings depending on the type of newlinerelationship in which some of them may be relevant while others may be newlineirrelevant to the users according to their perspective In the proposed newlinemethods irrelevant paths can be filtered using a suitable methodology while newlinediscovering the paths connecting entities or these irrelevant paths can be newlineranked lower during the ranking process In finding semantic associations newlinethe study consists of three proposed methods which help the users in newlineimproving the precision in a most appropriate manner newlineThe first one is ranking semantic association paths based on the newlineusers domain of interest using personalization in context specification In this newlineapproach the users interest level in various domains called semantic web newlineusage context is captured from the web browsing history of the users by newlineusing the personalization mechanism and it is incorporated in the ranking newlineformula newline newline