Relation representation based on private and shared features for adaptive few-shot link prediction

Although Knowledge Graphs (KGs) provide great value in many applications, they are often incomplete with many missing facts. KG Completion (KGC) is a popular technique for knowledge supplement. However, there are two fundamental challenges for KGC. One challenge is that few entity pairs are often available for most relations, and the other is that there exists complex relations, including one-to-many (1-N), many-to-one (N-1), and many-to-many (N-N). In this paper, we propose a new model to accomplish Few-shot KG Completion (FKGC) under complex relations, which is called Relation representation based on Private and Shared features for Adaptive few-shot link prediction (RPSA). In this model, we utilize the hierarchical attention mechanism for extracting the essential and crucial hidden information regarding the entity’s neighborhood so as to improve its representation. To enhance the representation of few-shot relations, we extract the private features (i.e., unique feature of each entity pair that represents the few-shot relation) and shared features (i.e., one or more commonalities among a few entity pairs that represent the few-shot relation). Specifically, a private feature extractor is used to extract the private semantic feature of the few-shot relation in the entity pair. After that, we design a shared feature extractor to extract the shared semantic features among a few reference entity pairs in the few-shot relation. Moreover, an adaptive aggregator aggregates several representations of the few-shot relation about the query. We conduct experiments on three datasets, including NELL-One, CoDEx-S-One and CoDEx-M-One datasets. According to the experimental results, the RPSA’s performance is better than that of the existing FKGC models. In addition, the RPSA model can also handle complex relations well, even in the few-shot scenario.