Knowledge graphs (KGs) are an important tool for representing complex
relationships between entities in the biomedical domain. Several methods have
been proposed for learning embeddings that can be used to predict new links in
such graphs. Some methods ignore valuable attribute data associated with
entities in biomedical KGs, such as protein sequences, or molecular graphs.
Other works incorporate such data, but assume that entities can be represented
with the same data modality. This is not always the case for biomedical KGs,
where entities exhibit heterogeneous modalities that are central to their
representation in the subject domain.
We propose a modular framework for learning embeddings in KGs with entity
attributes, that allows encoding attribute data of different modalities while
also supporting entities with missing attributes. We additionally propose an
efficient pretraining strategy for reducing the required training runtime. We
train models using a biomedical KG containing approximately 2 million triples,
and evaluate the performance of the resulting entity embeddings on the tasks of
link prediction, and drug-protein interaction prediction, comparing against
methods that do not take attribute data into account. In the standard link
prediction evaluation, the proposed method results in competitive, yet lower
performance than baselines that do not use attribute data. When evaluated in
the task of drug-protein interaction prediction, the method compares favorably
with the baselines. We find settings involving low degree entities, which make
up for a substantial amount of the set of entities in the KG, where our method
outperforms the baselines. Our proposed pretraining strategy yields
significantly higher performance while reducing the required training runtime.
Our implementation is available at this https URL .

提出了一种用于学习具有实体属性的知识图谱嵌入的模块化框架，其中模块可以对不同模态的属性数据进行编码，并且支持具有缺失属性的实体。在使用包含大约两百万三元组的生物医学 KG 进行训练和评估时，发现在给定低程度实体的情况下，与不考虑属性数据的基准方法相比，该方法在药物 - 蛋白质相互作用预测任务中的表现较好。