# Adaptive Attentional Network for Few-Shot Relational Learning of Knowledge Graphs
## 2 Related Work
### Embedding Models for Relational Learning  
Embedding models for relation learning can be mainly divided into two categories: translation-based models [14; 15; 16; 17; 18] and convolutional network-based models [19; 20; 21; 22; 23]. Most of the former methods first model the vector space of the knowledge graph and then infer the actual entities or relationships in the graph. RESCAL [14], DistMult [15], ComplEx [16], and RotatE [17] all want to mine the latent semantic features of triples and then model the relationship to infer the missing parts. TransE [18] is the first transfer-based representation model, and it is also an embedding-based base model, setting off a research boom in the Trans series. DistMult, ComplEx, and RotatE are proposed to further model the vector features of entities and relationships along the lines of TransE. In recent years, with the richness of neural network shapes, more and more models based on convolutional networks have been proposed. In order to solve the problem that the shallow link prediction model lacks the ability to extract deep features, blindly increasing the embedding_size will lead to overfitting. The ConvE [19] model first converts entity and relation vector representationsto 2-dimensional and cat-operates and performs dropout operation on the encoded data, then performs convolution, batch normalize, etc., and then goes through a fully connected layer and uses softmax to obtain the corresponding probability. Later, ConvR [20] uses the relation as the convolution kernel to perform two-dimensional convolution on the representation of the entity, which reduces the amount of parameters of the convolutional network and enriches the interaction between entities and relations. In order to solve the problem that ConvE does not pay attention to the global relationship of triples with the same dimension and ignores the translation characteristics of translation models, ConvKB [21] represents each triple as a three-column matrix, which is fed to the convolutional layer, and multiple The convolution kernel performs a convolution operation on it to output a feature vector, and the feature vector is multiplied by a weight vector to return the score of the triplet. Based on the idea of GCN information dissemination, R-GCN [22] is proposed as a model for knowledge graph embedding. The model uses the self-attention mechanism to solve the influence of different edge relationships on nodes, uses the convolution output of the upper layer as the convolution input of the lower layer, and designs different output layers for entity classification and link prediction tasks. To solve the problem of the inability to extract semantic features of different granularities, ConvKG [23] uses polymorphic filters to co-convolve triple embeddings to jointly learn semantic features of different granularities. Filters of different shapes cover different sizes on triple embeddings and capture different granularities of pairwise interactions between triples.  
The above embedding-based models usually assume sufficient training examples for all relations and entities and do not focus on those sparse symbols. Therefore, it is not suitable for the reasoning research of few-shot knowledge graphs. Compared with these methods, our model can handle long-tail or newly added relations and can adapt to both 1-shot relational learning and few-shot relational learning at the same time.