Adaptive Multi-hop Neighbor Selection for Few-Shot Knowledge Graph Completion

Few-shot Knowledge Graph Completion (FKGC) is a special task proposed for the relations with only a few triples. However, existing FKGC models face the following two issues: 1) these models cannot fully exploit the dynamic relation and entity properties of neighbors to generate discriminative representations; 2) these models cannot filter out noise in high-order neighbors to obtain reliable entity representations. In this paper, we propose an adaptive multi-hop neighbor selection model, namely AMBLE, to mitigate these two issues. Specifically, AMBLE first introduces a query-aware graph attention network (QAGAT) to obtain entity representations by dynamically aggregating one-hop neighbors based on relations and entities. Then, AMBLE aggregates high-order neighbors by iterating QAGAT and LSTM, which can efficiently extract useful and filter noisy information. Moreover, a Transformer encoder is used to learn the representations of subject and object entity pairs. Finally, we build an attentional matching network to map the query to few support triples. Experiments show that AMBLE outperforms state-of-the-art baselines on two public datasets.