Knowledge graph (KG) embedding has been used to benefit the diagnosis of
animal diseases by analyzing electronic medical records (EMRs), such as notes
and veterinary records. However, learning representations to capture entities
and relations with literal information in KGs is challenging as the KGs show
heterogeneous properties and various types of literal information. Meanwhile,
the existing methods mostly aim to preserve graph structures surrounding target
nodes without considering different types of literals, which could also carry
significant information. In this paper, we propose a knowledge graph embedding
model for the efficient diagnosis of animal diseases, which could learn various
types of literal information and graph structure and fuse them into unified
representations, namely LiteralKG. Specifically, we construct a knowledge graph
that is built from EMRs along with literal information collected from various
animal hospitals. We then fuse different types of entities and node feature
information into unified vector representations through gate networks. Finally,
we propose a self-supervised learning task to learn graph structure in pretext
tasks and then towards various downstream tasks. Experimental results on link
prediction tasks demonstrate that our model outperforms the baselines that
consist of state-of-the-art models. The source code is available at
this https URL

提出了一种用于动物疾病的高效诊断的知识图嵌入模型，该模型可以学习各种类型的文字信息和图结构，并将它们融合成统一的表示，通过门网络将不同类型的实体和节点特征信息融合为统一的向量表示，然后提出了一种自监督学习任务来预训练图结构，并在链接预测任务上击败了最先进模型组成的基线模型。