Many downstream inference tasks for knowledge graphs, such as relation prediction, have been handled successfully by knowledge graph embedding techniques in the transductive setting. To address the inductive setting wherein new entities are introduced into the knowledge graph at inference time, more recent work opts for models which learn implicit representations of the knowledge graph through a complex function of a network's subgraph structure, often parametrized by graph neural network architectures. These come at the cost of increased parametrization, reduced interpretability and limited generalization to other downstream inference tasks. In this work, we bridge the gap between traditional transductive knowledge graph embedding approaches and more recent inductive relation prediction models by introducing a generalized form of harmonic extension which leverages representations learned through transductive embedding methods to infer representations of new entities introduced at inference time as in the inductive setting. This harmonic extension technique provides the best such approximation, can be implemented via an efficient iterative scheme, and can be employed to answer a family of conjunctive logical queries over the knowledge graph, further expanding the capabilities of transductive embedding methods. In experiments on a number of large-scale knowledge graph embedding benchmarks, we find that this approach for extending the functionality of transductive knowledge graph embedding models to perform knowledge graph completion and answer logical queries in the inductive setting is competitive with--and in some scenarios outperforms--several state-of-the-art models derived explicitly for such inductive tasks.

翻译：许多知识图谱的下游推理任务（如关系预测）已在可迁移设定下通过知识图谱嵌入技术成功处理。为应对推理时新实体引入知识图谱的归纳设定，近期研究倾向于采用通过复杂网络子图结构函数（常由图神经网络架构参数化）学习知识图谱隐式表示的模型。然而，此类方法以参数化增加、可解释性降低及对其他下游推理任务的泛化能力受限为代价。本研究通过引入广义调和延拓形式，弥合传统可迁移知识图谱嵌入方法与新兴归纳关系预测模型之间的鸿沟——该方法利用可迁移嵌入方法学习的表示，推断归纳设定下推理时引入的新实体表示。此调和延拓技术提供最优近似解，可通过高效迭代方案实现，并用于回答知识图谱上的一类合取逻辑查询，进一步扩展可迁移嵌入方法的能力。在多个大规模知识图谱嵌入基准实验中，我们发现：这种扩展可迁移知识图谱嵌入模型功能的方法，在归纳设定下执行知识图谱补全与逻辑查询回答时，与专门为归纳任务设计的多个最优模型相比具有竞争力，且在某些场景中表现更优。