Research on Partitioning of Knowledge Graph Based on Query Semantic and NI-LPA
The current knowledge graph partition method without considering semantic knowledge will increase query commu-nication volume and decrease query execution efficiency after knowledge graph partition.In view of the fact that the knowledge in common query statements can be used to aggregate the substructures with high semantic correlation,and the NI-LPA(Node Impor-tance-Label Propagation Algorithm)has the characteristics of low time complexity and good partition quality,a knowledge graph partitioning method based on query semantics and NI-LPA is proposed.In this method,the semantic analysis of common SPARQL query sets is carried out,and the semantic correlation between nodes in the knowledge graph is calculated by using the analysis re-sults,and the correlation is combined with the importance of nodes representing structural characteristics in NI-LPA,so as to ob-tain the propagation strength between nodes,making it easier for important nodes with high semantic relevance to have the same la-bel.The experimental results show that compared with COPRA and NI-LPA algorithms,this method can not only reduce the edge cut rate and communication volume,but also effectively improve the query same-part rate with low redundancy.
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