An Intelligent Question Answering System based on Power Knowledge Graph
This work addresses the need for time-saving data search and reasoning in the electric power domain, but it is incremental as it applies existing methods like NLP and graph computing to a new domain-specific dataset.
The authors tackled the problem of efficient knowledge retrieval in the electric power domain by developing an intelligent question answering system based on a power knowledge graph, which enables high-speed multi-hop reasoning and accurate search from massive heterogeneous data.
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.