The task of inductive link prediction in knowledge graphs (KGs) generally focuses on test predictions with solely new nodes but not both new nodes and new relation types. In this work, we formally define the concept of double permutation-equivariant representations that are equivariant to permutations of both node identities and edge relation types. We then show how double-equivariant architectures are able to self-supervise pre-train on distinct KG domains and zero-shot predict links on a new KG domain (with completely new entities and new relation types). We also introduce the concept of distributionally double equivariant positional embeddings designed to perform the same task. Finally, we empirically demonstrate the capability of the proposed models against baselines on a set of novel real-world benchmarks. More interestingly, we show that self-supervised pre-training on more KG domains increases the zero-shot ability of our model to predict on new relation types over new entities on unseen KG domains.

翻译：知识图谱（KG）中的归纳式链路预测任务通常仅关注新节点的测试预测，而非同时包含新节点与新关系类型的场景。本研究正式定义了双置换等变表征的概念，该表征对节点身份与边关系类型的置换均具有等变性。我们进一步展示了双等变架构如何能够对跨领域KG进行自监督预训练，并在全新KG领域（包含完全未知的实体与新关系类型）实现零样本链路预测。同时引入分布性双等变位置嵌入的概念，用于执行相同任务。最后，我们通过一系列新颖的真实世界基准实验，实证了所提模型相较于基线方法的性能优势。更值得关注的是，研究表明在更多KG领域进行自监督预训练，将增强模型在未见KG领域中对新实体上的新关系类型进行零样本预测的能力。