  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.
