# An Intelligent Question Answering System based on Power Knowledge Graph

arXiv: https://arxiv.org/abs/2106.09013 · HTML: https://mlsanity.com/paper/2106.09013/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 21 references that are in the map.
- First posted on arXiv 16 Jun 2021.

## Builds on (most cited first, 1 of 1)

- [Enhancement of Power Equipment Management Using Knowledge Graph](https://mlsanity.com/paper/1904.12242/) (2019)

## Built on by (most cited first, 4 of 4)

- [Knowledge graph enhanced retrieval-augmented generation for failure mode and effects analysis](https://mlsanity.com/paper/2406.18114/) (2024)
- [HMSG: Heterogeneous Graph Neural Network based on Metapath Subgraph Learning](https://mlsanity.com/paper/2109.02868/) (2021)
- [A Novel Graph-Sequence Learning Model for Inductive Text Classification](https://mlsanity.com/paper/2512.20097/) (2025)
- [Seed-Anchored Budget-Bounded Graph Rendering for Question Answering on Industry-Standard Power-Grid Information and Exchange Models](https://mlsanity.com/paper/2609.02011/) (2026)

## Abstract (arXiv)

The intelligent question answering (IQA) system can accurately capture users' search intention by understanding the natural language questions, searching relevant content efficiently from a massive knowledge-base, and returning the answer directly to the user. Since the IQA system can save inestimable time and workforce in data search and reasoning, it has received more and more attention in data science and artificial intelligence. This article introduced a domain knowledge graph using the graph database and graph computing technologies from massive heterogeneous data in electric power. It then proposed an IQA system based on the electrical power knowledge graph to extract the intent and constraints of natural interrogation based on the natural language processing (NLP) method, to construct graph data query statements via knowledge reasoning, and to complete the accurate knowledge search and analysis to provide users with an intuitive visualization. This method thoroughly combined knowledge graph and graph computing characteristics, realized high-speed multi-hop knowledge correlation reasoning analysis in tremendous knowledge. The proposed work can also provide a basis for the context-aware intelligent question and answer.