  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.
