关键词: 知识图谱, 本体模型, 精准问答, 多层次数据

Abstract: [Purpose/significance] Aiming at problems of intelligent Q&A based on Q&A pairs such as low accuracy and resolution rate and poor user satisfaction, this paper constructs a knowledge graph (KG) ontology model that supports the realization of dynamic and accurate intelligent Q&A based on the knowledge graph. [Method/process] First, the paper analyzed the current problems and causes of intelligent question answering, and proposed a plan to build a knowledge graph to support intelligent question answering. Second, On the basis of existing ontology model construction methods, the paper proposed a multi-round loop method integrating multi-level data, which used the business data provided by the enterprises, user data and business system dynamic data as the data sources. And the core steps were to build a basic framework, improve the knowledge structure, and align three cycles of the knowledge structure. Finally, this paper took the domain of return and exchange as a case to describe the concrete steps of ontology model construction, from zero, added incrementally, and constructed ontology model of knowledge graph. [Result/conclusion] This paper applies the knowledge graph with the return ontology model as the schema layer in an intelligent Q&A system for testing. The evaluation results show that the accuracy rate increased by 50% and the precision rate increased by 300% after the return and exchange knowledge graph is online. So, the proposed ontology model construction method sorts out the complete and fine-grained domain knowledge structure from scattered domain knowledge, can provide accurate answers to users in intelligent Q&A, and can effectively solve the intelligent Q&A dilemma based on Q&A pairs.

Key words: knowledge graph, ontology model, accurate Q&A, multi-level data