Knowledge Extraction Algorithm Based on Hypergraphs

Knowledge extraction has always been one of the topics in computer science research.However,some existing know-ledge extraction methods are not sufficient to meet the practical needs in terms of visualization and latent knowledge extraction.It is well known that knowledge consists of definable knowledge and latent knowledge,and definable knowledge can be obtained while the latent knowledge is extracted,but not vice versa.Regarding the extraction of definable knowledge,many achievements have been made,but relatively less attention has been paid to the extraction of latent knowledge,especially how to extract latent knowledge through visualization methods,which is an urgent problem to be solved.Therefore,utilizing the visualization characte-ristics of hypergraphs in the context of information systems,this paper explores the correspondence between information systems and hypergraphs,and proposes methods for their mutual conversion.Using this method,combined with hypergraph theory and rough set theory,a pair of hypergraph-based upper and lower approximation operator is defined.Furthermore,the concept of approximate hypergraphs is proposed,and its properties are explored.The construction of approximate hypergraphs is completed,and an effective method for knowledge extraction under the hypergraph framework is implemented.By comparing with classical and recently proposed approximation theories and knowledge extraction methods,the advantages of the proposed method in terms of approximation and knowledge extraction are demonstrated.For the proposed method,its correctness is verified through practical examples,so that its applicability is indicated.The proposed method is a development and supplement to existing knowledge extraction theories.