SemRank: ranking complex relationship search results on the semantic web

TLDR: An approach that ranks results based on how predictable a result might be for users is presented, 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.

Abstract: 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 AssociationsIn 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