# Domain Knowledge Graph Question Answering Based on Semantic Analysis and Data Augmentation
## 6 Conclusions  
This paper presents a QA system based on a household registration domain KG using the household registration documents released by Wuhan city as an example. Considering the generality of the QA system, we divide the KG and QA system into two parts for separate construction: The KG processes the collected semi-structured and unstructured documents into structured data to establish a graph database that provides information support for the QA system. The QA system is trained on manually collected and data-augmented question sentence corpus to achieve accurate question parsing results, and then it retrieves answers from the KG. Policy changes and updates can be accommodated by re-establishing the KG to apply new policies and regulations. The QA system can adapt to different regional question expressions by adding question sentence corpus for further training. We validate the effectiveness of the QA system by comparing two semantic parsing steps. Our experimental results demonstrate that 56.71% of questions obtain a significant improvement in similarity calculation after reorganizing the sentence using  
Figure 12: The experimental box plot comparison. The red line represents the median, and the blue line represents mean.  
LTP. This approach can efficiently resolve problems caused by the lack of grammatical components and unclear expressions in oral language expression. Additionally, the RBMA model outperforms several widely used text classification models with an F1 value of 97.74%, precision of 99.49%, and recall of 96.15% in identifying question intentions. Finally, we apply the popular GPT3.5 generative language model to augment the dataset and reduce the impact of data imbalance. Our QA system achieves an accuracy of 87% in the unaugmented dataset and 93% in the augmented one after training.  
Although the QA system based on the household registration domain KG in this paper achieved certain performance results, it still has some limitations: (1) Due to the limited size of the question corpus dataset, despite the use of data augmentation, the amount of data remains insufficient, which limits the performance improvement of the model. We plan to collect more language resources or manually generate more questions to expand the dataset and improve the accuracy of the QA system. (2) In the event entity matching experiment, some questions experienced a decrease in similarity values after processing with LTP. Therefore, we will compare the similarity values before and after processing to ensure that sentences with normal expressions do not lose accuracy due to LTP processing. (3) Currently, for unrelated questions, we retrieve similar question in the question corpus. However, such a small-scale corpus often outputs irrelevant or completely erroneous answers. To address this issue, we plan to use other generative LLM to enhance the system's robustness and improve the performance of answering general questions.  
Conceptualization, S.H. and H.Z.; methodology, S.H.; software, S.H.; validation, S.H., H.Z. and W.Z.; formal analysis, W.Z.; investigation, W.Z.; resources, W.Z.; data curation, S.H.; writing--original draft preparation, S.H.; writing--review and editing, H.Z.; visualization, S.H.; supervision, H.Z.; project administration, H.Z.; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript. This work search was funded by the Science and Technology Department of Hubei Province, grant number 2022 BAA051.  
Not applicable.  
The authors declare no conflict of interest.