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.
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URL
https://arxiv.org/abs/2208.09137
