## 5 Conclusions  
This paper introduces a relation extraction model incorporating prompt information and the Top-K prediction set. Additionally, ChatGPT is employed to augment the training dataset, thereby improving the performance of the relation extraction model. The incorporation of prompt information aims to reduce the disparities between the pre-trained language model RoBERTa during its pre-training and fine-tuning stages, allowing for a more comprehensive utilization of the semantic information provided by the RoBERTa model. Furthermore, we observed that the predefined relationship categories in the SemEval-2010 Task 8 dataset are contingent on the relative positions of the head and tail entities. This dependency manifests as a challenge during experiments, where models may confuse entity positions, leading to erroneous results. To address this issue, we propose an entity position prediction task that assists the model in accurately identifying the relative positions of entities within sentences. As a result, the approach presented in this paper achieves an F1-score of 91.38 on the SemEval 2010 Task 8 dataset. In the future, we will focus on how to auto-generated prompts since, in our experiments, we found that different prompts impact the model's performance. Data augmentation is significant for supervised relational extraction models, and we will continue to explore how to utilize ChatGPT to accomplish data augmentation tasks better.  
Conceptualization, P.F. and D.O.; methodology, P.F. and H.W.; software, Y.W. and Z.Y.; validation, P.F., D.O. and H.W.; writing--original draft preparation, H.W.; writing--review and editing, P.F. and H.W.; visualization, P.F. and H.W.; funding acquisition, P.F. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Science and Technology Development Plan Project of Jilin Provincial Science and Technology Department (Key Technology Research on Risk Prediction and Assessment of Old Chronic Diseases Based on Medical Knowledge Graph (2023)B405L07).  
Publicly available datasets were used in this study. This data can be found here: [[http://www.kozareva.com/downloads.html](http://www.kozareva.com/downloads.html)]([http://www.kozareva.com/downloads.html](http://www.kozareva.com/downloads.html)) (accessed on 2 November 2023).  
We would like to express our deepest gratitude to all those who have contributed to the completion of this research and the writing of this paper.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CNN & Convolutional Neural Networks \\ GCN & Graph Convolutional Networks \\ PLM & Pre-trained Language Model \\ MLP & Multilayer Perceptron \\ BERT & Bidirectional Encoder Representations from Transformers \\ RoBERTa & Robustly Optimized BERT Pretraining Approach \\ NLP & Natural Language Processing \\ \end{tabular}  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **F1-Score** \\ \hline R-BERT & 89.25 \\ A-GCN & 89.85 \\ PTR & 89.9 \\ Skeleton-Aware BERT & 90.36 \\ KLCG & 90.5 \\ RIFRE & 91.3 \\ PTKRE & 91.38 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Results of model comparison experiments on the SemEval 2010 Task 8 dataset.