Knowledge atlas storage optimization algorithm based on two-level compression

Abstract

A knowledge graph is a special kind of graph data, which consists of a triad. Each node in the knowledge graph has several attributes and their attribute values. The storage of the knowledge graph has been the object of academic research, and in this paper, we conduct an in-depth study on the knowledge graph data indexing and compression storage algorithm supported by the RDF graph model, and propose an optimization algorithm for the storage query after the second-level compression. The core of this paper is that after the second-level compression of the k2-tree tree, the submatrices are prioritized in terms of the size of data blocks, and when retrieving data, they are retrieved according to the priority, so that the blocks in front are both subject and object at the same time, which can improve the efficiency of data reading, so that the parts with more information will be retrieved first, instead of the traditional sequential retrieval, which tends to retrieve the null values or the data with less information.