document: SemRank : ranking complex relationship search results on the semantic web While the idea that querying mechanisms for complex relationships ( otherwise known as Semantic Associations ) should be integral to Semantic Web search technologies has recently gained some ground , the issue of how search results will be ranked remains largely unaddressed . Since it is expected that the number of relationships between entities in a knowledge base will be much larger than the number of entities themselves , the likelihood that Semantic Association searches would result in an overwhelming number of results for users is increased , therefore elevating the need for appropriate ranking schemes . Furthermore , it is unlikely that ranking schemes for ranking entities ( documents , resources , etc. ) may be applied to complex structures such as Semantic Associations . In this paper , we present an approach that ranks results based on how predictable a result might be for users . It is based on a relevance model SemRank , which is a rich blend of semantic and information-theoretic techniques with heuristics that supports the novel idea of modulative searches , where users may vary their search modes to effect changes in the ordering of results depending on their need . We also present the infrastructure used in the SSARK system to support the computation of SemRank values for resulting Semantic Associations and their ordering .
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