In the field of representation learning on knowledge graphs (KGs), a hyper-relational fact consists of a main triple and several auxiliary attribute-value descriptions, which is considered more comprehensive and specific than a triple-based fact. However, currently available hyper-relational KG embedding methods in a single view are limited in application because they weaken the hierarchical structure that represents the affiliation between entities. To overcome this limitation, we propose a dual-view hyper-relational KG structure (DH-KG) that contains a hyper-relational instance view for entities and a hyper-relational ontology view for concepts that are abstracted hierarchically from the entities. This paper defines link prediction and entity typing tasks on DH-KG for the first time and constructs two DH-KG datasets, JW44K-6K, extracted from Wikidata, and HTDM based on medical data. Furthermore, we propose DHGE, a DH-KG embedding model based on GRAN encoders, HGNNs, and joint learning. DHGE outperforms baseline models on DH-KG, according to experimental results. Finally, we provide an example of how this technology can be used to treat hypertension. Our model and new datasets are publicly available.
翻译:在知识图表(KGs)的代表学习领域,超关系事实包含三重和若干辅助属性价值说明,被认为比三重事实更全面和具体,比三重事实更全面和具体。然而,目前单一观点中可用的超关系KG嵌入方法在应用上有限,因为它们削弱了代表各实体之间附属关系的等级结构。为了克服这一限制,我们提议了一个双视超关系KG结构(DH-KG),其中包含实体超关系实例视图,以及实体按等级顺序抽取的概念的超关系属性价值观点。DHGE首次界定了DH-KG的预测和输入任务的联系,并建立了两个DHKG数据集(JW44K-6K,摘自维基数据)和基于医学数据的HTDM。此外,我们提议DGE,一个基于GAN编码器的DH-K嵌入模型,以及联合学习。DGEGE将基准模型排出DHK公司的任务,我们最终用这个实验数据提供我们使用的新的实验结果。