A novel approach for discovering relevant semantic associations on social Web mining

Now-a-days, the primary focus of the search techniques in the first generation of the Web is accessing relevant documents from the Web. Though it satisfies user requirements, but it is insufficient as the user sometimes wishes to access actionable information involving complex relationships between two given entities. Finding such complex relationships (also known as semantic associations) is especially useful in applications such as National Security, Pharmacy and Business Intelligence etc. Therefore the next frontier is discovering relevant semantic associations between two entities present in large semantic metadata repositories. Given two entities, there exist a huge number of semantic associations between two entities. Hence ranking of these associations is required in order to find more relevant associations. For this Aleman Meza et al. proposed a method involving six metrics viz. context, subsumption, rarity, popularity, association length and trust. To compute the overall rank of the associations this method computes context, subsumption, rarity and popularity values for each component of the association and for all the associations. However it is obvious that, many components appears repeatedly in many associations therefore it is not necessary to compute context, subsumption, rarity and popularity values of the components every time for each association rather the previously computed values may be used while computing the overall rank of the associations. This paper proposes a method to reuse the previously computed values using a hash data structure thus reduce the execution time. To demonstrate the effectiveness of the proposed method, experiments were conducted on SWETO ontology. Results show that the proposed method is more efficient than the other existing methods.