Concepts and instances are important parts in knowledge graphs, but most knowledge graph embedding models treat them as entities equally, that leads to inaccurate embeddings of concepts and instances. Aiming to solve this problem, we propose a novel knowledge graph embedding model called JECI++ to jointly embed concepts and instances. First, JECI++ simplifies hierarchical concepts based on subClassOf relation and instanceOf relation, then re-links instances to the simplified concepts as new instanceOf triples. Consequently, an instance can be obtained by its neighbor instances and its belonging simplified concepts. Second, circular convolution is utilized to locate an instance in the embedding space, based on neighbor instances and simplified concepts. Finally, simplified concepts and instances are jointly embedded by the embeddings learner with CBOW (Continuous Bag-of-Words) and Skip-Gram strategies. Especially, JECI++ can alleviate the problem of complex relations by incorporating neighbor information of instances. JECI++ is evaluated by link prediction and triple classification on real world datasets. Experimental results demonstrate that it outperforms state-of-the-art models in most cases.

概念和实例是知识图中的重要部分，但是大多数知识图嵌入模型将它们平等地视为实体，这导致概念和实例的嵌入不准确。为了解决这个问题，我们提出了一种称为JECI ++的新颖的知识图嵌入模型，以联合嵌入概念和实例。首先，JECI ++根据subClassOf关系和instanceOf关系简化了层次结构概念，然后将实例作为新的instanceOf重新链接到简化的概念三重。因此，实例可以通过其邻居实例及其所属的简化概念来获得。其次，基于邻居实例和简化概念，利用循环卷积在嵌入空间中定位实例。最后，嵌入的学习者通过CBOW（连续词袋）和Skip-Gram策略共同嵌入了简化的概念和实例。特别是，JECI ++可以通过合并实例的邻居信息来减轻复杂关系的问题。通过对真实数据集的链接预测和三重分类来评估JECI ++。实验结果表明，它在大多数情况下都优于最新模型。