Few-Shot Knowledge Graph Completion

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.

Adaptive Attentional Network for Few-Shot Knowledge Graph Completion

“…Previous few-shot learning studies mainly focus on computer vision (Sung et al, 2018), imitation learning (Duan et al, 2017) and sentiment analysis . Recent attempts Chen et al, 2019;Zhang et al, 2020) tried to perform few-shot relational learning for long-tail relations. proposed a matching network GMatching, which is the first research on one-shot learning for KGs as far as we know.…”

“…GMatching exploits a neighbor encoder to enhance entity embeddings from their one-hop neighbors, and uses a LSTM matching processor to perform a multi-step matching by a LSTM block. FSRL (Zhang et al, 2020) extends GMatching to few-shot cases, further capturing local graph structures with an attention mechanism. Chen et al (2019) proposed a novel meta relational learning framework MetaR by extracting and transferring shared knowledge across tasks from a few existing facts to incomplete ones.…”

“…given only a few entity pairs of the task relation r. These known few-shot entity pairs associated with r are called references. To improve semantical representations of the references, and Zhang et al (2020) devise modules to enhance entity embeddings with their local graph neighbors. The former simply assumes that all neighbors contribute equally to the entity embedding, and in this way the neighbors are always weighted identically.…”

“…Specifically, given a task relation with its reference/query triples, FAAN proposes an adaptive attentional neighbor encoder to model entity representations with one-hop entity neighbors. Unlike the previous neighbor encoder with a fixed attention map in (Zhang et al, 2020), we allow attention scores dynamically adaptive to the task relation under the translation assumption. This will capture the diverse roles of entities through varied impacts of neighbors.…”

Towards Locality-Aware Meta-Learning of Tail Node Embeddings on Networks

“…These methods typically learn some prior knowledge from an abundant number of related tasks, and adapt the prior to new tasks with limited data. As many forms of data often entail inherent graph structures, recent studies often exploit some auxiliary graphs, such as affinity graphs [10,22], class relational graphs [21] and one-hop ego-networks [37,42]. Few-shot learning on graphs proper have also been explored, including node classification on a single large graph [43], and node or graph-level classification on multiple graphs [5,40].…”