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
this https URL

本研究介绍了一个名为 DBLPLink 的网络应用程序，它在 DBLP 学术知识图上执行实体链接。DBLPLink 使用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范围。实体候选者从数据库中根据标签获取，并且实体重排器根据实体嵌入（如 TransE、DistMult 和 ComplEx）对它们进行排序。结果显示出来，用户可以在 T5-small、T5-base 和不同的 KG 嵌入之间进行比较和对比。该演示可在此 https 网址访问。

DBLPLink：DBLP 学术知识图谱的实体链接器

DBLPLink: An Entity Linker for the DBLP Scholarly Knowledge Graph

In this work we create a question answering dataset over the DBLP scholarly
knowledge graph (KG). DBLP is an on-line reference for bibliographic
information on major computer science publications that indexes over 4.4
million publications published by more than 2.2 million authors. Our dataset
consists of 10,000 question answer pairs with the corresponding SPARQL queries
which can be executed over the DBLP KG to fetch the correct answer. DBLP-QuAD
is the largest scholarly question answering dataset.

本文在 DBLP 学术知识图谱上创建了一个问答数据集，其中包括 10,000 个问题答案对以及相应的 SPARQL 查询，可在 DBLP KG 上执行以获取正确答案。DBLP-QuAD 是最大的学术问答数据集。

DBLP-QuAD: 一个基于 DBLP 学术知识图谱的问答数据集

DBLP-QuAD: A Question Answering Dataset over the DBLP Scholarly Knowledge Graph

DBLP is the largest open-access repository of scientific articles on computer
science and provides metadata associated with publications, authors, and
venues. We retrieved more than 6 million publications from DBLP and extracted
pertinent metadata (e.g., abstracts, author affiliations, citations) from the
publication texts to create the DBLP Discovery Dataset (D3). D3 can be used to
identify trends in research activity, productivity, focus, bias, accessibility,
and impact of computer science research. We present an initial analysis focused
on the volume of computer science research (e.g., number of papers, authors,
research activity), trends in topics of interest, and citation patterns. Our
findings show that computer science is a growing research field (approx. 15%
annually), with an active and collaborative researcher community. While papers
in recent years present more bibliographical entries in comparison to previous
decades, the average number of citations has been declining. Investigating
papers' abstracts reveals that recent topic trends are clearly reflected in D3.
Finally, we list further applications of D3 and pose supplemental research
questions. The D3 dataset, our findings, and source code are publicly available
for research purposes.

本研究提取了 DBLP 上的 600 多万篇计算机科学论文，分析了计算机科学研究的趋势、活动水平、协作情况等方面。该研究的发现是计算机科学是一个快速发展的研究领域，但近年来其平均引用次数有所下降，并且最新的主题趋势也在 DBLP 数据集中得到了呈现。