Recent years have witnessed great advancement of representation learning (RL)-based models for the knowledge graph relation prediction task. However, they generally rely on structure information embedded in the encyclopedic knowledge graph, while the beneficial semantic information provided by lexical knowledge graph is ignored, leading the problem of shallow understanding and coarse-grained analysis for knowledge acquisition. Therefore, this article introduces concept information derived from the lexical knowledge graph (e.g., Probase), and proposes a novel Hierarchical Attention model for Relation Prediction, which consists of entity-level attention mechanism and concept-level attention mechanism, to throughly integrate multiple semantic signals. Experimental results demonstrate the efficiency of the proposed method on two benchmark datasets.

中文翻译：

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竖琴

近年来，用于知识图关系预测任务的基于表示学习 (RL) 的模型取得了巨大进步。然而，它们普遍依赖于嵌入在百科知识图谱中的结构信息，而忽略了词汇知识图谱提供的有益语义信息，导致了对知识获取的理解浅层和粗粒度分析的问题。因此，本文引入了从词汇知识图谱（如Probase）衍生的概念信息，并提出了一种新颖的关系预测的Hierarchical Attention模型，它由实体级注意机制和概念级注意机制组成，通过整合多种语义信号。实验结果证明了该方法在两个基准数据集上的有效性。