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:我们建议一种实体-不可知代表制学习方法,用以处理通过嵌入知识图而带来的低效率参数存储成本问题。 常规知识图将地图元素嵌入知识图中,包括实体和关系,通过分配一个或多个特定的嵌入器(即矢量代表制)进入连续矢量空间。 因此,嵌入参数的数量随着知识图的增长而线性地增加。 在我们提议的模型“实体-不可知度代表制学习”中,我们只学习一小批实体的嵌入器,并把它们称为保留实体。 为了获得全套实体的嵌入器,我们将它们的可辨别信息与关联关系、 k- 最接近的保留实体和多窗口邻居进行编码。 我们学习了将可辨别信息转换为实体嵌入器的普遍和实体- 不可知性编码器。 这种方法使我们提议的 EARL 能够有一个静态、高效和较低的参数计数, 而不是常规知识图嵌入法。 实验结果显示, EAR使用比基线更少的参数和更好地进行连接的预测任务,反映其参数效率。