# Knowledge Graph Construction in Power Distribution Networks

arXiv: https://arxiv.org/abs/2311.08724 · HTML: https://mlsanity.com/paper/2311.08724/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 21 references that are in the map.
- First posted on arXiv 15 Nov 2023.

## Builds on (most cited first, 3 of 3)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Leveraging Deep Neural Networks and Knowledge Graphs for Entity Disambiguation](https://mlsanity.com/paper/1504.07678/) (2015)
- [Modeling Relational Information in Question-Answer Pairs with Convolutional Neural Networks](https://mlsanity.com/paper/1604.01178/) (2016)

## Abstract (arXiv)

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.