Relation-oriented few-shot knowledge graph prototype networks

Abstract

Knowledge graph (KG) embedding has been widely researched, but it suffers from some problems in the few-shot scenarios. The topological and semantic connection among entities cannot satisfy the assumption of samples’ independent identically distribution. Additionally, these relations between entity pairs are various and complex. But most of the previous methods are node-oriented, which is weak in modeling complex relations. In order to solve the above problems, we propose a few-shot relation prototype network (FRPN). In our method, each relation is regarded as a learning object, instead of just entity pairs’ concatenation. A relation-oriented two-channel embedding mechanism is designed to achieve multi-scale information aggregation at the entity level and the relation level respectively. For the entity level, it aggregates information at different scales according to the relation type and the neighbors under each relation. For the relation level, our model obtains each kind of relation’s prototype from the ontology and the entity layer. The multi-scale aggregation contributes to KG embedding in the few-shot scenario. Compared with existing models, our model has significantly improved the performance on both the link prediction and triple classification tasks in two few-shot datasets.