We explore link prediction as a proxy for automatically surfacing documents
from existing literature that might be topically or contextually relevant to a
new document. Our model uses transformer-based graph embeddings to encode the
meaning of each document, presented as a node within a citation network. We
show that the semantic representations that our model generates can outperform
other content-based methods in recommendation and ranking tasks. This provides
a holistic approach to exploring citation graphs in domains where it is
critical that these documents properly cite each other, so as to minimize the
possibility of any inconsistencies

我们探讨了链接预测作为自动获取与新文档的主题或背景相关的现有文献的代理方法。我们的模型使用基于变压器的图嵌入来编码每个文档的含义，这些文档作为引文网络中的节点呈现。我们展示了我们的模型生成的语义表示在推荐和排名任务中可以胜过其他基于内容的方法。这为在那些需要这些文档正确互相引用以最小化可能出现的不一致性的领域中探索引文图提供了一种整体的方法。