We propose an entity-agnostic representation learning method for handling the problem of inefficient parameter storage costs brought by embedding knowledge graphs. Conventional knowledge graph embedding methods map elements in a knowledge graph, including entities and relations, into continuous vector spaces by assigning them one or multiple specific embeddings (i.e., vector representations). Thus the number of embedding parameters increases linearly as the growth of knowledge graphs. In our proposed model, Entity-Agnostic Representation Learning (EARL), we only learn the embeddings for a small set of entities and refer to them as reserved entities. To obtain the embeddings for the full set of entities, we encode their distinguishable information from their connected relations, k-nearest reserved entities, and multi-hop neighbors. We learn universal and entity-agnostic encoders for transforming distinguishable information into entity embeddings. This approach allows our proposed EARL to have a static, efficient, and lower parameter count than conventional knowledge graph embedding methods. Experimental results show that EARL uses fewer parameters and performs better on link prediction tasks than baselines, reflecting its parameter efficiency.

翻译：我们提出一种实体无关的表示学习方法，用于解决知识图谱嵌入带来的参数存储成本低效问题。传统的知识图谱嵌入方法通过为实体和关系分配一个或多个特定嵌入（即向量表示），将知识图谱中的元素映射到连续向量空间。因此，嵌入参数的数量随知识图谱的增长而线性增加。在我们提出的实体无关表示学习（EARL）模型中，我们仅学习一小部分实体（称为保留实体）的嵌入。为获取全部实体的嵌入，我们从其连接的关系、k近邻保留实体以及多跳邻居中编码区分性信息。我们学习通用且实体无关的编码器，将区分性信息转换为实体嵌入。该方法使得EARL相比传统知识图谱嵌入方法具有静态、高效且参数数量更低的特性。实验结果表明，EARL在链接预测任务中使用更少的参数且性能优于基线模型，体现了其参数高效性。