Extracting relational triplets aims at detecting entity pairs and their semantic relations. Compared with pipeline models, joint models can reduce error propagation and achieve better performance. However, all of these models require large amounts of training data, therefore performing poorly on many long-tail relations in reality with insufficient data. In this article, we propose a novel end-to-end model, called TGIN, for few-shot triplet extraction. The core of TGIN is a multilayer heterogeneous graph with two types of nodes (entity node and relation node) and three types of edges (relation–entity edge, entity–entity edge, and relation–relation edge). On the one hand, this heterogeneous graph with entities and relations as nodes can intuitively extract relational triplets jointly, thereby reducing error propagation. On the other hand, it enables the triplet information of limited labeled data to interact better, thus maximizing the advantage of this information for few-shot triplet extraction. Moreover, we devise a graph aggregation and update method that utilizes translation algebraic operations to mine semantic features while retaining structure features between entities and relations, thereby improving the robustness of the TGIN in a few-shot setting. After updating the node and edge features through layers, TGIN propagates the label information from a few labeled examples to unlabeled examples, thus inferring triplets from these unlabeled examples. Extensive experiments on three reconstructed datasets demonstrate that TGIN can significantly improve the accuracy of triplet extraction by 2.34%~10.74% compared with the state-of-the-art baselines. To the best of our knowledge, we are the first to introduce a heterogeneous graph for few-shot relational triplet extraction.

中文翻译：

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TGIN：用于少样本关系三元组提取的基于翻译的图推理网络


提取关系三元组旨在检测实体对及其语义关系。与管道模型相比，联合模型可以减少错误传播并获得更好的性能。然而，所有这些模型都需要大量的训练数据，因此在现实中数据不足的许多长尾关系上表现不佳。在本文中，我们提出了一种新颖的端到端模型，称为 TGIN，用于小样本三元组提取。 TGIN的核心是一个多层异构图，具有两种类型的节点（实体节点和关系节点）和三种类型的边（关系-实体边、实体-实体边和关系-关系边）。一方面，这种以实体和关系为节点的异构图可以直观地联合提取关系三元组，从而减少错误传播。另一方面，它使得有限标记数据的三元组信息能够更好地交互，从而最大限度地发挥该信息在小样本三元组提取中的优势。此外，我们设计了一种图聚合和更新方法，利用平移代数运算来挖掘语义特征，同时保留实体和关系之间的结构特征，从而提高 TGIN 在少数镜头设置中的鲁棒性。通过层更新节点和边特征后，TGIN 将标签信息从一些标记示例传播到未标记示例，从而从这些未标记示例中推断出三元组。对三个重建数据集的大量实验表明，与最先进的基线相比，TGIN 可以显着提高三联体提取的准确性 2.34%~10.74%。 据我们所知，我们是第一个引入异构图进行少样本关系三元组提取的人。