Knowledge Graph Completion (KGC) has been proposed to improve Knowledge Graphs by filling in missing connections via link prediction or relation extraction. One of the main difficulties for KGC is a low resource problem. Previous approaches assume sufficient training triples to learn versatile vectors for entities and relations, or a satisfactory number of labeled sentences to train a competent relation extraction model. However, low resource relations are very common in KGs, and those newly added relations often do not have many known samples for training. In this work, we aim at predicting new facts under a challenging setting where only limited training instances are available. We propose a general framework called Weighted Relation Adversarial Network, which utilizes an adversarial procedure to help adapt knowledge/features learned from high resource relations to different but related low resource relations. Specifically, the framework takes advantage of a relation discriminator to distinguish between samples from different relations, and help learn relation-invariant features more transferable from source relations to target relations. Experimental results show that the proposed approach outperforms previous methods regarding low resource settings for both link prediction and relation extraction.
翻译:建议通过链接预测或关系提取填补缺失的连接,从而改进知识图(KGC),从而改进知识图(KGC)。KGC面临的一个主要困难是资源低的问题。以前的办法假定有足够的三重培训,为实体和关系学习多种矢量的矢量,或有令人满意的标记的句子,以培训合格的关系提取模型。然而,在KGs, 低资源关系非常常见,而这些新增加的关系往往没有许多已知的培训样本。在这项工作中,我们的目标是在具有挑战性的环境中预测新的事实,因为那里只有有限的培训实例。我们提议了一个称为Weighted Relation Aversarial网络的一般框架,这个框架利用对抗性程序帮助将从高资源关系中学到的知识/特点适应不同的但相关的低资源关系。具体地说,该框架利用关系区分器区分不同关系的样本,帮助了解从源关系到目标关系的关联性特征。实验结果表明,拟议的方法比以前关于联系预测和关系提取的低资源环境的方法要好。