Knowledge graph (KG) embedding encodes the entities and relations from a KG into low-dimensional vector spaces to support various applications such as KG completion, question answering, and recommender systems. In real world, knowledge graphs (KGs) are dynamic and evolve over time with addition or deletion of triples. However, most existing models focus on embedding static KGs while neglecting dynamics. To adapt to the changes in a KG, these models need to be retrained on the whole KG with a high time cost. In this paper, to tackle the aforementioned problem, we propose a new context-aware Dynamic Knowledge Graph Embedding (DKGE) method which supports the embedding learning in an online fashion. DKGE introduces two different representations (i.e., knowledge embedding and contextual element embedding) for each entity and each relation, in the joint modeling of entities and relations as well as their contexts, by employing two attentive graph convolutional networks, a gate strategy, and translation operations. This effectively helps limit the impacts of a KG update in certain regions, not in the entire graph, so that DKGE can rapidly acquire the updated KG embedding by a proposed online learning algorithm. Furthermore, DKGE can also learn KG embedding from scratch. Experiments on the tasks of link prediction and question answering in a dynamic environment demonstrate the effectiveness and efficiency of DKGE.
翻译:知识图形( KG) 嵌入将实体和关系从 KG 嵌入到低维矢量空间的知识图形( KG), 以支持 KG 完成、 问答、 推荐系统等各种应用。 在现实世界中, 知识图形( KG) 是动态的, 并随着时间的增加或删除而演变。 然而, 大多数现有模型侧重于嵌入静态的 KG, 同时忽略动态。 为了适应KG 的变化, 这些模型需要以高时间成本在整个 KG 上重新培训。 在本文中, 为了解决上述问题, 我们提出了一种新的有环境觉悟的动态知识图形嵌入( DKGE) 方法, 支持在线学习。 在实体和关系及其背景的联合模型中, DGE 引入了两种不同的表达方式( 知识嵌入和背景元素嵌入 ) 。 为了适应整个 KGE GE 更新 K 更新的影响力, 因此, DGE 将 快速地从 D 图像中学习一个动态的 K 。