Intelligent Question Answering System Design with Domain-Specific Knowledge Graphs

With the fast development of the mobile internet, various data has been generated explosively in recent years. To solve the data redundancy problem caused by IoE, knowledge graph construction is considered one of the indispensable techniques for performing systematic and accurate representation of data, especially in some specific domains. In this paper, we propose a construction method of panoramic domain-specific knowledge graphs in various domains, which treat sensitive (secret) data, e.g., medical and industrial domains. This method mainly uses the web crawler to obtain data from relevant web pages by category and saves the obtained data as a structured JSON file in the form of dictionaries. This paper takes the military field as an example to construct the domain-specific knowledge graph based on our proposed method. Specifically, the specific domain knowledge graph is stored in Neo4j and MongoDB to provide an intuitive and applied representation of knowledge, respectively. Based on the knowledge graph stored in MongoDB, we develop an intelligent question-answering system in a specific domain, which can better satisfy the information retrieval and knowledge learning of related personnel. Moreover, the template-based question-answering system is designed to effectively solve the problem of semantic repetition of questions. Finally, the constructed knowledge graph and question-answering system are evaluated and analyzed.