Abstract: Grid computing allows the sharing of resources from the heterogeneous and distributed locations. An extensive study of the scientific research area in grid computing declares the effective retrieval systems design is the prerequisite for an efficiency improvement. Information Retrieval (IR) systems require the periodic updating due to the rapid increase in a number of multimedia data. Generally, extraction of information in IR system based on the matching of appropriate given queries. The retrieving information from a keyword or matching string based IR is insufficient and limited to critical information by the user. The raising up of geospatial data in semantic makes the IR systems as real-time development. The geospatial data can be used in many scientific fields such as agriculture, land use and climate change. The review of real semantic web describes the problem of poor updating. The large amounts of geospatial data are archived in multiple data center. Caching improves the retrieval performance in the widely distributed environment whereas the performance is poor in the large data set. In the case of spatial data, the geospatial semantic web identifies which parts of geospatial information need to receive semantic specifications in order to achieve interoperability. The duplication and redundant nodes exist in the two or more nodes during the tree construction process caused the irrelevance results in response to the queries. The simultaneous verification of overlap and the weight adjustment in proposed scheme in Globus toolkit environment enhances the relevance results. We use the ranking of trivial similarity measure based ontology structure to improve the efficiency of the data retrieval. Ranking of similarity measures we assures the sorting of the list. To avoid the repetition of the distributed query results from the sorted list we introduce Markov-Trivial-Tree (MTT) based index prediction process to capture the repeated results. The comparative analysis between the proposed MTT-based ontology structures proves that it offers better results than the traditional methods regarding the precision, recall, F-measure, and accuracy for diverse large dimension datasets.