Knowledge Graphs (KGs) have been applied to many tasks including Web search, link prediction, recommendation, natural language processing, and entity linking. However, most KGs are far from complete and are growing at a rapid pace. To address these problems, Knowledge Graph Completion (KGC) has been proposed to improve KGs by filling in its missing connections. Unlike existing methods which hold a closed-world assumption, i.e., where KGs are fixed and new entities cannot be easily added, in the present work we relax this assumption and propose a new open-world KGC task. As a first attempt to solve this task we introduce an open-world KGC model called ConMask. This model learns embeddings of the entity's name and parts of its text-description to connect unseen entities to the KG. To mitigate the presence of noisy text descriptions, ConMask uses a relationship-dependent content masking to extract relevant snippets and then trains a fully convolutional neural network to fuse the extracted snippets with entities in the KG. Experiments on large data sets, both old and new, show that ConMask performs well in the open-world KGC task and even outperforms existing KGC models on the standard closed-world KGC task.

本文介绍了一种名为ConMask的开放世界的知识图谱补全模型，该模型可以学习实体名称和其文本描述的嵌入以将未见过的实体连接到知识图谱中，并通过使用基于关系的内容掩蔽提取相关摘要以训练完全卷积神经网络来融合数据。实验证明，ConMask在开放世界的知识图谱补全任务中表现良好，并且在标准的闭式世界知识图谱补全任务上甚至优于现有的补全模型。