论文标题
KG-NSF：以不含样本的方法完成知识图完成
KG-NSF: Knowledge Graph Completion with a Negative-Sample-Free Approach
论文作者
论文摘要
知识图（kg）完成是一项重要任务，它极大地使许多领域的知识发现受益（例如生物医学研究）。近年来，学习kg嵌入以执行此任务的嵌入引起了很大的关注。尽管KG嵌入方法取得了成功，但它们主要使用负抽样，从而增加了计算复杂性以及由于封闭的世界假设而引起的偏见预测。为了克服这些局限性，我们建议\ textbf {kg-nsf}，这是一个基于嵌入向量的互相关矩阵学习kg嵌入的无负抽样框架。结果表明，所提出的方法在更快地收敛的同时，达到了与基于阴性抽样的方法的可比性链接预测性能。
Knowledge Graph (KG) completion is an important task that greatly benefits knowledge discovery in many fields (e.g. biomedical research). In recent years, learning KG embeddings to perform this task has received considerable attention. Despite the success of KG embedding methods, they predominantly use negative sampling, resulting in increased computational complexity as well as biased predictions due to the closed world assumption. To overcome these limitations, we propose \textbf{KG-NSF}, a negative sampling-free framework for learning KG embeddings based on the cross-correlation matrices of embedding vectors. It is shown that the proposed method achieves comparable link prediction performance to negative sampling-based methods while converging much faster.
