论文标题
GreenKGC：轻量级知识图完成方法
GreenKGC: A Lightweight Knowledge Graph Completion Method
论文作者
论文摘要
知识图完成（KGC）旨在发现知识图（kgs）中实体之间的缺失关系。大多数先前的KGC工作都专注于通过简单的评分功能学习实体和关系的嵌入。但是，通常需要更高维度的嵌入空间才能获得更好的推理能力，这会导致更大的模型大小，并阻碍对现实世界中的问题的适用性（例如，大型kgs或移动/边缘计算）。在这项工作中提出了一种称为GreenKGC的轻量级模块化解决方案，以解决此问题。 GreenKGC由三个模块组成：表示学习，特征修剪和决策学习，以提取判别KG特征，并使用分类器和负面抽样对缺失关系做出准确的预测。实验结果表明，在低维度，GreenKGC在大多数数据集中都能胜过SOTA方法。此外，对于较小的模型大小的高维模型，低维的GreenKGC可以实现竞争性甚至更好的性能。
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.
