GREENKGC: A LIGHTWEIGHT KNOWLEDGE GRAPH COMPLETION METHOD

Yun-Cheng Wang, Xiou Ge, Bin Wang, C.-C. Jay Kuo

Abstract:

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.