基于字面感知医学知识图谱表示学习的伴侣动物疾病诊断
Companion Animal Disease Diagnostics based on Literal-aware Medical Knowledge Graph Representation Learning
AI总结：
提出LiteralKG知识图谱嵌入模型，融合字面信息与图结构进行伴侣动物疾病诊断，在链路预测任务中优于现有最先进基线。
摘要：
知识图谱（KG）嵌入已被用于通过分析电子病历（EMRs，如笔记和兽医记录）来辅助动物疾病诊断。然而，学习能够捕获KG中实体及其带有字面信息关系的表示具有挑战性，因为KG表现出异构特性和多种类型的字面信息。同时，现有方法大多旨在保留围绕目标节点的图结构，而未考虑不同类型的字面信息，这些字面信息也可能携带重要信息。在本文中，我们提出了一种用于高效诊断动物疾病的知识图谱嵌入模型，该模型能够学习各种类型的字面信息和图结构，并将它们融合为统一表示，即LiteralKG。具体而言，我们构建了一个由EMRs构建的知识图谱，以及从各种动物医院收集的字面信息。然后，我们通过门控网络将不同类型的实体和节点特征信息融合为统一向量表示。最后，我们提出了一种自监督学习任务，在 pretext 任务中学习图结构，随后应用于各种下游任务。在链路预测任务上的实验结果表明，我们的模型优于由最先进模型组成的基线。源代码可在 https://github.com/NSLab-CUK/LiteralKG 获取。
英文摘要：
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 https://github.com/NSLab-CUK/LiteralKG.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
