  Knowledge graph completion (KGC) aims to discover missing relationships
between entities in knowledge graphs (KGs). Most prior KGC work focuses on
learning embeddings for entities and relations through a simple scoring
function. Yet, a higher-dimensional embedding space is usually required for a
better reasoning capability, which leads to a larger model size and hinders
applicability to real-world problems (e.g., large-scale KGs or mobile/edge
computing). A lightweight modularized KGC solution, called GreenKGC, is
proposed in this work to address this issue. GreenKGC consists of three
modules: representation learning, feature pruning, and decision learning, to
extract discriminant KG features and make accurate predictions on missing
relationships using classifiers and negative sampling. Experimental results
demonstrate that, in low dimensions, GreenKGC can outperform SOTA methods in
most datasets. In addition, low-dimensional GreenKGC can achieve competitive or
even better performance against high-dimensional models with a much smaller
model size.
