Research on an Information Encoding Technology for Representing Complex Tensor

This article addresses the characteristics of high-dimensional knowledge graphs, such as large node quantities, high attribute dimensions, numerous relationship types, and changes over time. It constructs entity relationship attributes by studying the multidimensional similarity between massive network target data and entity target data. A tensor model is used for the unified representation of high-dimensional knowledge graph data, resulting in a target relationship tensor model construction process. A tensor model for target similarity relations, which contains multidimensional information and refined structure, is established. The high-dimensional heterogeneous networks are transformed into relationship matrices to achieve knowledge conversion and unified expression modeling. This supports the unified representation of knowledge graphs for unstructured, semi-structured, and structured data, providing support for the engineering application of unified representations of high-dimensional knowledge graphs. This article employs the Tucker decomposition method to achieve low-dimensional representations of high-dimensional vectors and provides a detailed analysis of the decomposition process and algorithm. Experiments show that a three-dimensional tensor can be decomposed into two-dimensional factor matrices, achieving a unified representation of complex tensor information.