Few-Shot Knowledge Graph Completion

TL;DR

This paper introduces FSRL, a novel few-shot learning model for knowledge graph completion that effectively leverages limited relation data to discover new facts, outperforming existing methods.

Contribution

The paper proposes a new few-shot relation learning model that captures knowledge from heterogeneous graphs and aggregates reference representations for improved KG completion.

Findings

FSRL outperforms state-of-the-art methods on two datasets.

Effective in discovering facts with very few relation references.

Captures knowledge from heterogeneous graph structures.

Abstract

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.