Foundation models in language and vision have the ability to run inference on any textual and visual inputs thanks to the transferable representations such as a vocabulary of tokens in language. Knowledge graphs (KGs) have different entity and relation vocabularies that generally do not overlap. The key challenge of designing foundation models on KGs is to learn such transferable representations that enable inference on any graph with arbitrary entity and relation vocabularies. In this work, we make a step towards such foundation models and present ULTRA, an approach for learning universal and transferable graph representations. ULTRA builds relational representations as a function conditioned on their interactions. Such a conditioning strategy allows a pre-trained ULTRA model to inductively generalize to any unseen KG with any relation vocabulary and to be fine-tuned on any graph. Conducting link prediction experiments on 57 different KGs, we find that the zero-shot inductive inference performance of a single pre-trained ULTRA model on unseen graphs of various sizes is often on par or better than strong baselines trained on specific graphs. Fine-tuning further boosts the performance.

翻译：语言和视觉领域的基础模型得益于可迁移的表示（如语言中的标记词汇表），能够对所有文本和视觉输入进行推理。知识图谱具有不同的实体和关系词汇表，且通常不重叠。设计知识图谱基础模型的关键挑战在于学习这种可迁移的表示，从而能够在任意包含非重叠实体和关系词汇的图上进行推理。在这项工作中，我们向此类基础模型迈出了一步，提出了ULTRA方法，一种学习通用且可迁移图表示的方法。ULTRA将关系表示构建为基于其交互的函数条件。这种条件化策略使预训练的ULTRA模型能够归纳性地泛化到任意包含任意关系词汇的未见知识图谱上，并可在任何图上进行微调。我们在57个不同知识图谱上进行链接预测实验，发现单个预训练ULTRA模型在各类规模未见图上的零样本归纳推理性能通常与针对特定图训练的强基模型相当或更优。微调可进一步提升性能。