Knowledge graph embedding (KGE) that maps entities and relations into vector representations is essential for downstream applications. Conventional KGE methods require high-dimensional representations to learn the complex structure of knowledge graph, but lead to oversized model parameters. Recent advances reduce parameters by low-dimensional entity representations, while developing techniques (e.g., knowledge distillation or reinvented representation forms) to compensate for reduced dimension. However, such operations introduce complicated computations and model designs that may not benefit large knowledge graphs. To seek a simple strategy to improve the parameter efficiency of conventional KGE models, we take inspiration from that deeper neural networks require exponentially fewer parameters to achieve expressiveness comparable to wider networks for compositional structures. We view all entity representations as a single-layer embedding network, and conventional KGE methods that adopt high-dimensional entity representations equal widening the embedding network to gain expressiveness. To achieve parameter efficiency, we instead propose a deeper embedding network for entity representations, i.e., a narrow entity embedding layer plus a multi-layer dimension lifting network (LiftNet). Experiments on three public datasets show that by integrating LiftNet, four conventional KGE methods with 16-dimensional representations achieve comparable link prediction accuracy as original models that adopt 512-dimensional representations, saving 68.4% to 96.9% parameters.

翻译：知识图谱嵌入（KGE）将实体和关系映射为向量表示，是下游应用的关键。传统KGE方法需要高维表示来学习知识图谱的复杂结构，但会导致模型参数过大。近年来的进展通过低维实体表示减少参数，同时开发技术（如知识蒸馏或重构表示形式）来补偿维度降低带来的性能损失。然而，此类操作引入的复杂计算和模型设计可能无法适用于大规模知识图谱。为寻求提升传统KGE模型参数效率的简单策略，我们从更深层神经网络的特性中获得启发：在组合结构上，深层网络实现与宽层网络相当的表达能力仅需指数级更少的参数。我们将所有实体表示视为单层嵌入网络，传统KGE方法采用高维实体表示等同于拓宽嵌入网络以获取表达能力。为实现参数效率，我们提出采用更深的实体表示嵌入网络，即窄实体嵌入层加多层维度提升网络（LiftNet）。在三个公开数据集上的实验表明，通过集成LiftNet，四种传统KGE方法在16维表示下可达到原模型采用512维表示时的链接预测精度，节省68.4%至96.9%的参数。