Knowledge Graph Construction in Power Distribution Networks
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866911765973958656 |
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| author | Li, Xiang Wang, Che Li, Bing Chen, Hao Li, Sizhe |
| author_facet | Li, Xiang Wang, Che Li, Bing Chen, Hao Li, Sizhe |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_08724 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Knowledge Graph Construction in Power Distribution Networks Li, Xiang Wang, Che Li, Bing Chen, Hao Li, Sizhe Computation and Language Machine Learning 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. |
| title | Knowledge Graph Construction in Power Distribution Networks |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2311.08724 |