Few-shot Knowledge Graph Completion with Adaptive Negative Sampling Mechanism

Few-shot knowledge graph completion (few-shot KGC) mines unseen knowledge by leveraging meta-learning and contrastive learning to achieve accurate predictions with limited triples. Recent studies have focused on designing distance or similarity metrics to provide better knowledge representation between entities and relations. However, three issues with negative sampling remain unexplored: 1) the construction of negative queries heavily relies on manual experience in selecting candidate tail entities, 2) the constructed negative queries may mislabel potential true facts, and 3) the varying difficulties of negative queries are ignored. To solve the above issues, in this paper, we introduce curriculum learning into few-shot KGC and propose a novel few-shot KGC framework empowered by an adaptive negative sampling mechanism, which can eliminate the dependence on any additional manual experience, reduce mislabeling, and generate negative queries with appropriate difficulty. Specifically, the proposed framework includes two alternating phases. In the negative sampling phase, we first design a novel positive-unlabeled learning based scoring function with a type-related candidates encoder and then build a variable-speed sliding window based pacing function to select negative queries with appropriate learning difficulty under current training step. In the meta-training phase, we develop an adapted triple-oriented knowledge encoder to provide accurate representation for queries. Experimental results demonstrate that the proposed framework outperforms the state-of-the-art baselines and provides negative queries with appropriate difficulty in few-shot KGC.