Graph computing based knowledge reasoning in electric power system considering knowledge graph sparsity

Knowledge graph, which is a rapidly developing technology, allows for a strong support in business and engineering. Knowledge graph plays an important role in recommendations and decision-making, while in the electric power industry there would be more possibilities for knowledge graph to be utilized. However, as a complex cause and effect network, the electric power domain knowledge graph has massive nodes, heterogeneous edges, and sparse structure, thus it requires human effort to process data, while the quality and the accuracy cannot be guaranteed. We propose a novel graph computing-based knowledge reasoning method that takes into account the sparsity of the electric power domain knowledge graph, to solve the above problems and achieve improved accuracy of graph classification and knowledge reasoning tasks. The Haar basis is constructed to realize fast calculation, while the multi-scale network structure is introduced to assure the classification accuracy and generalization. We ran simulations and analyzed the results on the NCI-1, CEPRIUHVP, and CEPRI_EQUIP databases, and it demonstrated that our proposed algorithm showed an outstanding performance considering accuracy and loss.