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The volume of published linked open datasets in RDF format is growing exponentially in the last decades. With this continuous proliferation of this growth, demands for managing, accessing, and compressing the RDF dataset have become increasingly important. Most approaches are focused on the structured compression technique while a very few researches have been done for compact representation of the RDF dataset. In this paper, we have proposed an efficient rule mining and compression approach for RDF datasets through various meaningful semantic association rules determined from the RDF graph. We have introduced grammar-based pattern system, clustering of rules, rules pruning, and Top-k scheme to improve the expressiveness of rule patterns, identify the similarity within the random pair of rules, extract the most delicate rules, find the accurate mining threshold, and efficiently learn the rules …