GreenKGC: A Lightweight Knowledge Graph Completion Method
This addresses the issue of large model sizes hindering applicability to real-world problems like large-scale knowledge graphs or mobile/edge computing, though it appears incremental as it builds on existing KGC approaches.
The paper tackles the problem of knowledge graph completion by proposing GreenKGC, a lightweight method that reduces model size while maintaining performance, achieving competitive or better results against high-dimensional models with a smaller footprint.
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.