An Intelligent Question Answering System based on Power Knowledge Graph

TL;DR

This paper presents an intelligent question answering system leveraging a power knowledge graph, enabling accurate, fast, and context-aware responses in the electric power domain through advanced NLP and graph computing techniques.

Contribution

It introduces a domain-specific knowledge graph for electric power and develops an IQA system that combines NLP, knowledge reasoning, and graph computing for precise answers.

Findings

Achieved high-speed multi-hop reasoning in large knowledge graphs.

Enabled accurate intent and constraint extraction from natural language questions.

Provided intuitive visualization of answers for users.

Abstract

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.