# Link Prediction Based on Feature Mapping and Bi-Directional Convolution
###### Abstract  
A considerable amount of research on link prediction has recently been driven by missing relationships between knowledge graph entities and the problem of the incompleteness of knowledge graphs. Some recent studies have shown that convolutional neural networks based on knowledge embeddings are highly expressive and have good performance in link prediction. However, we found that the convolutional neural network (CNN)-based models do not handle the link between relations and entities well. For this reason, this paper proposes a link prediction model (LPM) based on feature mapping and bi-directional convolution. For the modeling of the task, an encoding layer-mapping layer-decoding layer structure is used. Among these layers, the encoding layer adopts a graph attention network to encode multi-hop triad information and obtains richer encoding of entities and relationships. The mapping layer can realize the mapping transformation between entities and relations and project the entity encoding in the space of relation encoding to capture the subtle connection between entities and relations. The decoding layer adopts bidirectional convolution to merge and decode the triples in a sequential inverse order, which makes the decoding layer model more advantageous in prediction. In addition, the decoding layer also adopts the r-drop training method to effectively reduce the distribution error generated by training between models and enhance the robustness of the model. Our experiments demonstrated the effectiveness of mapping relations, bidirectional convolution, and r-drop, and the accuracy of the proposed model showed significant improvements for each evaluation metric on two datasets, WN18 RR and FB15k-237. knowledge graph; link prediction; knowledge embedding; feature mapping; convolutional neural network 20242089202420892024 have achieved satisfactory results in link prediction. However, the current knowledge embedding methods [7] do not take into account the subtle links between entities and relationships, although they contain multi-hop ternary information with high quality. Multi-hop ternary information is used to add multiple intermediate entities and relationships between the head entity, relationship, and tail entity; adding new entities and relationships can constitute more triads. In addition, existing convolutional neural network models mainly perform convolutional operations on the ternary directly while ignoring the sequential relationships between entities and relations in the ternary. Based on the above observations, this paper proposes a link prediction model based on feature mapping and bidirectional convolution. (1) Firstly, the multi-hop ternary information is fed into the graph attention network to realize the encoding of entities and relations. (2) After encoding entities and relationships, the entities are mapped according to the space of relationship encoding to acquire the encoding of entities in the space of relationships, which can capture the links between entities and relationships. (3) After the mapped entity-relationship encoding is obtained, the ternary is combined in two sequences in reverse order, and the combined ternary is encoded and then fed into the convolutional neural network, which is trained by the r-drop method. The bi-directionally combined ternary will be more advantageous in predicting the head and tail entities after the information is extracted by the convolutional neural network. Our architecture model includes a coding layer, mapping layer, and decoding layer, in which the multi-hop graph neural coding layer, the entity relationship transformation layer, and the bidirectional convolutional layer play the roles of coding layer, mapping layer, and decoding layer, respectively. The contributions of this paper are as follows. To the best of our knowledge, this paper introduces, for the first time, the mapping of entities and relations in a convolutional neural network for dealing specifically with the problem of link prediction on knowledge graphs.