Few-Shot Knowledge Graph Completion
Few-Shot Knowledge Graph Completion
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DOI：
10.1609/aaai.v34i03.5698
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Chuxu Zhang;Huaxiu Yao;Chao Huang;Meng Jiang;Z. Li;N. Chawla
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作者：
Chuxu Zhang;Huaxiu Yao;Chao Huang;Meng Jiang;Z. Li;N. Chawla
知识图（KGs）是各种自然语言处理应用的有用资源。先前的KG完成方法需要大量的训练实例（即，头尾实体对）。真实的情况是，对于大多数关系，只有很少的实体对可用。现有的一次学习方法限制了该方法在少次场景下的推广性，且没有充分利用监控信息，而少次KG完备化方法还没有得到很好的研究。在这项工作中，我们提出了一种新的少镜头关系学习模型（FSRL），旨在发现事实的新关系与少镜头的参考。FSRL可以有效地从异构图结构中捕获知识，聚合少量引用的表示，并为每个关系匹配引用集的相似实体对。在两个公共数据集上的大量实验表明，FSRL优于最先进的。
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.