In semantic computing, Match is an operator that takes as an input two graph-like structures; it can be database schemas or XML schemas and generates a mapping between the corresponding nodes of the two graphs. In semantic schema matching, we attempt to explore the mappings between the two schemas; based on their semantics by employing any semantic similarity measure. In this study, we have defined taxonomy of all possible semantic similarity measures; moreover we also proposed an approach that exploits semantic relations stored in the DBpedia dataset while utilizing a hybrid ranking system to dig out the similarity between nodes of the two graphs.

In last decade of computing, a growing popularity for semantic cache schemes has been observed. Numerous techniques have been proposed for improved performance of semantic cache. Semantic cache is aimed towards reducing the network traffic load with the ability to address some queries without contacting the server. Semantic cache overcomes the limitations of previous page-caching and tuple-cache techniques. A prime concern of semantic cache includes query processing (query response-time) and cache management. The efficiency of semantic cache can be improved by using semantic indexing sachems. Semantic indexing sachems are segment based and hierarchical semantic indexing scheme. Both these sachems can improve the efficiency of query processing and cache management. In this paper, we have performed the evaluation of index schemes for Semantic Cache through the experimental study. Evaluations of these schemes are available in literature but experimental study is not available. Through this experiment, we have highlighted which technique is most suited in what kind of scenario.