Knowledge graph completion (KGC) aims to discover missing relationships
between entities in knowledge graphs (KGs). Most prior KGC work focuses on
learning representations for entities and relations. 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: 1) representation learning, 2) feature
pruning, and 3) decision learning. In Module 1, we leverage existing KG
embedding models to learn high-dimensional representations for entities and
relations. In Module 2, the KG is partitioned into several relation groups
followed by a feature pruning process to find the most discriminant features
for each relation group. Finally, a classifier is assigned to each relation
group to cope with low-dimensional triple features for KGC tasks in Module 3.
We evaluate the performance of GreenKGC on four widely used link prediction
datasets and observe that GreenKGC can achieve comparable or even better
performance against original high-dimensional embeddings with a much smaller
model size. Furthermore, we experiment on two triple classification datasets to
demonstrate that the same methodology can generalize to more tasks.

本文提出了一种轻量级模块化知识图谱完成 (GreenKGC) 方案，该方案包含三个模块：表示学习、特征修剪和决策学习，旨在解决在获得更好推理能力的同时保持较小的模型大小。实验结果表明，GreenKGC 可以在较小的模型尺寸下达到与高维嵌入方法相当甚至更好的性能，并且可以推广到更多任务中。