# Domain Knowledge Graph Question Answering Based on Semantic Analysis and Data Augmentation
## 1 Introduction  
The question answering (QA) system is designed to provide users with personalized information services through human-computer interaction in the form of question-and-answer by analyzing the user's input. As one of the core tasks of artificial intelligence, QA has attracted extensive attention due to its widespread application in natural language processing and information retrieval [1]. Information retrieval-based question answering (IRQA) and knowledge-based question answering (KBQA) are the main forms of QA systems [2]. IRQA is known as open-domain QA: it can answer the questions that come from any domain. This type of QA system retrieves relevant texts from a large amount of passage based on the user's given question using information retrieval methods. Simply using retrieved texts as the answer is not precise enough. Thanks to the breakthroughs in recent years in the large language models (LLM), such as GPT-4 [3] and ChatGLM [4], which can better understand natural language questions, integrating LLM into QA systems can provide users with more comprehensive answers. However, because the answers generated by LLM have a certain degree of randomness, it cannot guarantee that the answers will always be consistent with the retrieved texts. In some special domains (such as medicine, policy, law, etc. ), QA systems are required to provide accurate and rigorous answers to ensure that the system can provide reliable and authoritative information. On the other hand, KBQA systems process unstructured or semi-structured passages into structured database storage. They construct query templates through semantic analysis of the questions, and the final answers are also retrieved from these texts. As a result, the answers obtained are consistent with the retrieved texts, providing high accuracy andprecision. The household registration domain studied in this paper is a policy subfield, which requires a strict one-to-one correspondence between the answer and the original text to avoid misleading users or producing adverse consequences. Therefore, we adopt the knowledge-based paradigm to construct a household registration QA system to ensure that users obtain accurate and rigorous answers. The household registration system is one of the most fundamental social management systems in the world, mainly involving matters such as birth, death, migration, marriage, divorce, adoption, and disappearance. For instance, Japan revised the Family Registration Law [5] in 1871, and the Act for Registering Births, Deaths, and Marriages in England [6] has been continuously used in the UK since 1838 and has undergone several revisions. Although the names of household registration laws and regulations in countries around the world are different, their actual content are similar. In recent years, with the development of urbanization and population growth in many developing countries, the handling of household registration in various complex situations has led to continuous updates in related policies with more explicit explanations. For example, the required documents for handling birth registration for newborns may vary depending on the marital status and ethnic composition of the parents. However, due to limitations in users' expression ability and a lack of information query channels, how to accurately and quickly retrieve the household registration information that users need has become an urgent problem to be solved. The primary objective of this paper is to build a QA system for household registration to provide users with accurate and reliable household policy information, improve the efficiency of household registration services, and speed up the processing time. In household registration text data, the handling information of the same event entity is different under various constraints. Such complex semantic information will lead to multi-constraint query problems in answer retrieval when mapped into a triple form.