In this work, we present a web application named DBLPLink, which performs entity linking over the DBLP scholarly knowledge graph. DBLPLink uses text-to-text pre-trained language models, such as T5, to produce entity label spans from an input text question. Entity candidates are fetched from a database based on the labels, and an entity re-ranker sorts them based on entity embeddings, such as TransE, DistMult and ComplEx. The results are displayed so that users may compare and contrast the results between T5-small, T5-base and the different KG embeddings used. The demo can be accessed at https://ltdemos.informatik.uni-hamburg.de/dblplink/.

翻译：本文提出一个名为DBLPLink的Web应用程序，用于对DBLP学术知识图谱执行实体链接。DBLPLink采用基于文本到文本的预训练语言模型（如T5），从输入文本问题中生成实体标签片段。系统根据这些标签从数据库中检索候选实体，并通过实体重新排序器（基于TransE、DistMult和ComPlEx等实体嵌入）对候选实体进行排序。最终结果以可视化方式呈现，使用户能够对比T5-small、T5-base以及不同知识图谱嵌入方法的效果差异。演示入口可访问https://ltdemos.informatik.uni-hamburg.de/dblplink/。