Adaptive Attentional Network for Few-Shot Knowledge Graph Completion
This work addresses the challenge of querying unseen facts in knowledge graphs with limited data, offering an incremental improvement over static representation methods.
The paper tackles the problem of few-shot knowledge graph completion by proposing an adaptive attentional network that learns dynamic representations of entities and references, achieving new state-of-the-art results in link prediction on two public datasets with various few-shot sizes.
Few-shot Knowledge Graph (KG) completion is a focus of current research, where each task aims at querying unseen facts of a relation given its few-shot reference entity pairs. Recent attempts solve this problem by learning static representations of entities and references, ignoring their dynamic properties, i.e., entities may exhibit diverse roles within task relations, and references may make different contributions to queries. This work proposes an adaptive attentional network for few-shot KG completion by learning adaptive entity and reference representations. Specifically, entities are modeled by an adaptive neighbor encoder to discern their task-oriented roles, while references are modeled by an adaptive query-aware aggregator to differentiate their contributions. Through the attention mechanism, both entities and references can capture their fine-grained semantic meanings, and thus render more expressive representations. This will be more predictive for knowledge acquisition in the few-shot scenario. Evaluation in link prediction on two public datasets shows that our approach achieves new state-of-the-art results with different few-shot sizes.