Few-Shot Knowledge Graph Completion
This addresses the challenge of few-shot learning for knowledge graph completion, which is crucial for real-world applications where data is scarce, but it is incremental in improving existing few-shot approaches.
The paper tackles the problem of knowledge graph completion with limited training data by proposing a novel few-shot relation learning model (FSRL), which outperforms state-of-the-art methods on two public datasets.
Knowledge graphs (KGs) serve as useful resources for various natural language processing applications. Previous KG completion approaches require a large number of training instances (i.e., head-tail entity pairs) for every relation. The real case is that for most of the relations, very few entity pairs are available. Existing work of one-shot learning limits method generalizability for few-shot scenarios and does not fully use the supervisory information; however, few-shot KG completion has not been well studied yet. In this work, we propose a novel few-shot relation learning model (FSRL) that aims at discovering facts of new relations with few-shot references. FSRL can effectively capture knowledge from heterogeneous graph structure, aggregate representations of few-shot references, and match similar entity pairs of reference set for every relation. Extensive experiments on two public datasets demonstrate that FSRL outperforms the state-of-the-art.