Knowledge Graph Construction in Power Distribution Networks
This work addresses a domain-specific problem for power distribution network management, with incremental improvements in entity linking accuracy.
The paper tackles the problem of constructing knowledge graphs for power distribution networks by matching entities from dispatch texts to the graph using an enhanced CNN model, achieving high overall accuracy compared to baselines in real-world scenarios.
In this paper, we propose a method for knowledge graph construction in power distribution networks. This method leverages entity features, which involve their semantic, phonetic, and syntactic characteristics, in both the knowledge graph of distribution network and the dispatching texts. An enhanced model based on Convolutional Neural Network, is utilized for effectively matching dispatch text entities with those in the knowledge graph. The effectiveness of this model is evaluated through experiments in real-world power distribution dispatch scenarios. The results indicate that, compared with the baselines, the proposed model excels in linking a variety of entity types, demonstrating high overall accuracy in power distribution knowledge graph construction task.