Learning Relation-Specific Representations for Few-shot Knowledge Graph Completion

Recent years have witnessed increasing interest in few-shot knowledge graph completion (FKGC), which aims to infer unseen query triples for a few-shot relation using a handful of reference triples of the relation. The primary focus of existing FKGC methods lies in learning the relation representations that can reflect the common information shared by the query and reference triples. To this end, these methods learn the embeddings of entities with their direct neighbors, and use the concatenation of the entity embeddings as the relation representations. However, the entity embeddings learned only from direct neighborhoods may have low expressiveness when the entity has sparse neighbors or shares a common local neighborhood with other entities. Moreover, the embeddings of two entities are insufficient to represent the semantic information of their relationship, especially when they have multiple relations. To address these issues, we propose a Relation-Specific Context Learning (RSCL) framework, which exploits graph contexts of triples to capture the semantic information of relations and entities simultaneously. Specifically, we first extract graph contexts for each triple, which can provide long-term entity-relation dependencies. To model the graph contexts, we then develop a hierarchical relation-specific learner to learn global and local relation-specific representations for relations by capturing contextualized information of triples and incorporating local information of entities. Finally, we utilize the learned representations to predict the likelihood of the query triples. Experimental results on two public datasets demonstrate that RSCL outperforms state-of-the-art FKGC methods.