# Link Prediction Based on Feature Mapping and Bi-Directional Convolution
## 6 Conclusions  
This paper presents a link prediction model based on feature mapping and bidirectional convolution. The model first splices multi-hop and single-hop information and encodes it through a graph attention network to obtain an encoded representations of entities and relationships. Then, the model maps entities into the relationship coding space. Finally, the model splices the forward and inverse order triples and decodes them using a convolutional neural network, which is combined with r-drop for training. The model achieved good results on the WN18 RR and FB15k-237 datasets. However, the model has some limitations. First, the model uses a bi-directional convolutional and attentional mechanism during encoding and decoding, which may be limited by the localization of information transfer. Second, the model does not consider the encoding of multi-hop graph attention networks and the decoding of graph neural networks, which may limit the expressive power of the model. Therefore, in further research, the use of graph neural networks in the encoding and decoding process could be explored to  
Figure 5: Results of MR. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Model** & \multicolumn{4}{c}{**WN18 RR**} & \multicolumn{4}{c}{**FB15K-237**} \\ \hline  & MR & MRR & @1 & @3 & @10 & MR & MRR & @1 & @3 & @10 \\ ConGAT-BI & 2322 & 0.451 & 38.4 & 49.5 & 58.9 & 173 & 0.514 & 43.2 & 54.7 & 65.3 \\ ConGAT-BI–FM & 1451 & 0.470 & 39.2 & 50.6 & 59.6 & 148 & **0.515** & 43.8 & 55.2 & 65.9 \\ ConGAT-BI-FM–rDrop & **1421** & **0.475** & **40.2** & **51.1** & **60.7** & **126** & **0.515** & **45.2** & **55.6** & **66.6** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The specific results of the ablation experiments, with the best results highlighted in bold. improve the performance of the model. Future research directions could consider using graph neural networks for decoding based on feature mapping and multi-hop graph attention network encoding to achieve consistency in encoding and decoding models. This combined use of the same network structure may improve the representation and generalization capabilities of the model and further enhance the performance of the link prediction task. In addition, other improvements could be explored, such as introducing more sophisticated attention mechanisms or using more advanced pre-trained models to enhance model performance. These research directions are expected to promote the development and application of link prediction models in practical applications. Conceptualization, P.F. and D.O. ; methodology, P.F. and X.Z. ; software, H.W., Y.W. and Z.Y. ; validation, P.F., D.O. and X.Z. ; writing--original draft preparation, X.Z. ; writing--review and editing, P.F. and X.Z. ; visualization, P.F. and X.Z. ; 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 JB405L07)). Publicly available datasets were used in this study. This data can be found here (accessed on 1 March 2024): [[https://huggingface.co/datasets/VLyb/WN18](https://huggingface.co/datasets/VLyb/WN18) RR]([https://huggingface.co/datasets/VLyb/WN18](https://huggingface.co/datasets/VLyb/WN18) RR); [[https://huggingface.co/datasets/VLyb/FB15k-237](https://huggingface.co/datasets/VLyb/FB15k-237)]([https://huggingface.co/datasets/VLyb/FB15k-237](https://huggingface.co/datasets/VLyb/FB15k-237)). 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 conflicts of interest. The following abbreviations are used in this manuscript: