Knowledge graph embeddings are dense numerical representations of entities in a knowledge graph (KG). While the majority of approaches concentrate only on relational information, i.e., relations between entities, fewer approaches exist which also take information about literal values (e.g., textual descriptions or numerical information) into account. Those which exist are typically tailored towards a particular modality of literal and a particular embedding method. In this paper, we propose a set of universal preprocessing operators which can be used to transform KGs with literals for numerical, temporal, textual, and image information, so that the transformed KGs can be embedded with any method. The results on the kgbench dataset with three different embedding methods show promising results.

翻译：知识图谱嵌入是将知识图谱中实体表示为密集数值向量的技术。尽管大多数方法仅关注关系信息（即实体间的关系），但较少有方法同时考虑文字值信息（例如文本描述或数值信息）。现有的方法通常针对特定的文字模态和特定的嵌入方法进行定制。本文提出了一组通用预处理算子，可用于转换包含数值、时间、文本和图像信息的文字值知识图谱，从而使转换后的知识图谱能够通过任意方法进行嵌入。在kgbench数据集上使用三种不同嵌入方法进行实验，结果显示出良好的效果。