Knowledge graph entity typing (KGET) is a task to predict the missing entity
types in knowledge graphs (KG). Previously, KG embedding (KGE) methods tried to
solve the KGET task by introducing an auxiliary relation, 'hasType', to model
the relationship between entities and their types. However, a single auxiliary
relation has limited expressiveness for diverse entity-type patterns. We
improve the expressiveness of KGE methods by introducing multiple auxiliary
relations in this work. Similar entity types are grouped to reduce the number
of auxiliary relations and improve their capability to model entity-type
patterns with different granularities. With the presence of multiple auxiliary
relations, we propose a method adopting an Asynchronous learning scheme for
Entity Typing, named AsyncET, which updates the entity and type embeddings
alternatively to keep the learned entity embedding up-to-date and informative
for entity type prediction. Experiments are conducted on two commonly used KGET
datasets to show that the performance of KGE methods on the KGET task can be
substantially improved by the proposed multiple auxiliary relations and
asynchronous embedding learning. Furthermore, our method has a significant
advantage over state-of-the-art methods in model sizes and time complexity.

知识图谱实体类型（KGET）是一项用于预测知识图谱（KG）中缺失实体类型的任务。我们通过引入多个辅助关系来提高 KG 嵌入方法的表达能力，将相似的实体类型分组以减少辅助关系的数量，并改进不同粒度实体类型模式的建模能力。我们提出了 AsyncET 方法，采用异步学习方案进行实体类型预测，通过交替更新实体和类型嵌入来保持学习到的实体嵌入的最新性和信息性。实验结果表明，所提出的多个辅助关系和异步嵌入学习方法显著改善了 KGE 方法在 KGET 任务上的性能，而且我们的方法在模型大小和时间复杂度方面具有显著优势。

AsyncET: 带有辅助关系的知识图谱实体类型异步学习

AsyncET: Asynchronous Learning for Knowledge Graph Entity Typing with  Auxiliary Relations

In the last decades, people have been consuming and combining more drugs than
before, increasing the number of Drug-Drug Interactions (DDIs). To predict
unknown DDIs, recently, studies started incorporating Knowledge Graphs (KGs)
since they are able to capture the relationships among entities providing
better drug representations than using a single drug property. In this paper,
we propose the medicX end-to-end framework that integrates several drug
features from public drug repositories into a KG and embeds the nodes in the
graph using various translation, factorisation and Neural Network (NN) based KG
Embedding (KGE) methods. Ultimately, we use a Machine Learning (ML) algorithm
that predicts unknown DDIs. Among the different translation and
factorisation-based KGE models, we found that the best performing combination
was the ComplEx embedding method with a Long Short-Term Memory (LSTM) network,
which obtained an F1-score of 95.19% on a dataset based on the DDIs found in
DrugBank version 5.1.8. This score is 5.61% better than the state-of-the-art
model DeepDDI. Additionally, we also developed a graph auto-encoder model that
uses a Graph Neural Network (GNN), which achieved an F1-score of 91.94%.
Consequently, GNNs have demonstrated a stronger ability to mine the underlying
semantics of the KG than the ComplEx model, and thus using higher dimension
embeddings within the GNN can lead to state-of-the-art performance.

该论文介绍了一种名为 medicX 的端到端框架，它将来自公共药物库的多种药物特征集成到一个知识图谱中，并使用各种翻译、分解和神经网络方法嵌入图谱中的节点，最终使用机器学习算法来预测未知的药物相互作用。