Abstract
The smooth operation of the power grid is closely related to the national economy and people’s livelihood. The knowledge graph, as a widely-used technology, has made considerable contributions to power grid dispatching and query answering. However, explainable reasoning on grid defects datasets is still of great challenge, most models cannot balance effectiveness and explainablity. Therefore, their assistance in grid defects diagnosis is minimal. To address this issue, we propose the rule-enhanced cognitive graph for power grid knowledge reasoning. Our model consists of two modules: expansion and reasoning. For the expansion module, we take into consideration that path-based methods often ignore graph structure and global information and combine the local cognitive graph and global degree distribution. For the reasoning module, we provide reasoning evidence from two aspects: logical rule learning for strong evidence and cognitive reasoning for possible paths. Experiment results on our grid defects dataset make known that our model achieves better performance with explainablity.
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Acknowledgment
This work is supported by Major Program of Xiamen (3502Z20231006); National Natural Science Foundation of China (62176227, U2066213); Fundamental Research Funds for the Central Universities (20720210047).
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Zhang, Y. et al. (2023). Explainable Knowledge Reasoning on Power Grid Knowledge Graph. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science(), vol 14090. Springer, Singapore. https://doi.org/10.1007/978-981-99-4761-4_59
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