Knowledge bases are collections of domain-specific and commonsense facts. Recently, the sizes of KBs are rocketing due to automatic extraction for knowledge and facts. For example, the number of facts in WikiData is up to 974 million! According to our observation, current KBs, especially domain KBs, show strong relevance in relations according to some topics[1], [2]. These patterns can be used to conclude and infer for part of facts in the KBs. Therefore, the original KBs can be minimzed by extracting patterns and essential facts. In this paper, we introduce a framework for extracting knowledge essence and reducing overall volume of KBs by mining semantic patterns in relations. Facts are formalized as first-order predicates and patterns are induced as Horn rules. Table I and Rule (1), (2) show an example of such extraction. By extracting the rules from listed facts, both table Ib and Ic can be inferred from other tables and then be removed. The remaining is organized as follows: Section II analysed properties of rules as equivalence classes. Essence extraction problem is formally defined in Section III. And Section IV introduces the basic framework for essence extraction. Finally Section V concludes the paper.
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