In recent years, scholarly data has grown dramatically in terms of both scale
and complexity. It becomes increasingly challenging to retrieve information
from scholarly knowledge graphs that include large-scale heterogeneous
relationships, such as authorship, affiliation, and citation, between various
types of entities, e.g., scholars, papers, and organizations. As part of the
Scholarly QALD Challenge, this paper presents a question-answering (QA) system
called NLQxform, which provides an easy-to-use natural language interface to
facilitate accessing scholarly knowledge graphs. NLQxform allows users to
express their complex query intentions in natural language questions. A
transformer-based language model, i.e., BART, is employed to translate
questions into standard SPARQL queries, which can be evaluated to retrieve the
required information. According to the public leaderboard of the Scholarly QALD
Challenge at ISWC 2023 (Task 1: DBLP-QUAD - Knowledge Graph Question Answering
over DBLP), NLQxform achieved an F1 score of 0.85 and ranked first on the QA
task, demonstrating the competitiveness of the system.

这篇研究论文介绍了一种名为 NLQxform 的问答系统，它基于变压器模型 BART，可以通过自然语言界面访问学术知识图谱，实现复杂查询意图的转换和信息检索。在 Scholarly QALD Challenge 中，NLQxform 在 QA 任务上获得 0.85 的 F1 分数，并在排行榜上名列第一，展示了系统的竞争力。

NLQxform: 基于语言模型的问题到 SPARQL 的转换器

NLQxform: A Language Model-based Question to SPARQL Transformer

Scholarly data is growing continuously containing information about the
articles from a plethora of venues including conferences, journals, etc. Many
initiatives have been taken to make scholarly data available as Knowledge
Graphs (KGs). These efforts to standardize these data and make them accessible
have also led to many challenges such as exploration of scholarly articles,
ambiguous authors, etc. This study more specifically targets the problem of
Author Name Disambiguation (AND) on Scholarly KGs and presents a novel
framework, Literally Author Name Disambiguation (LAND), which utilizes
Knowledge Graph Embeddings (KGEs) using multimodal literal information
generated from these KGs. This framework is based on three components: 1)
Multimodal KGEs, 2) A blocking procedure, and finally, 3) Hierarchical
Agglomerative Clustering. Extensive experiments have been conducted on two
newly created KGs: (i) KG containing information from Scientometrics Journal
from 1978 onwards (OC-782K), and (ii) a KG extracted from a well-known
benchmark for AND provided by AMiner (AMiner-534K). The results show that our
proposed architecture outperforms our baselines of 8-14% in terms of the F1
score and shows competitive performances on a challenging benchmark such as
AMiner. The code and the datasets are publicly available through Github:
this https URL and
Zenodo:this https URL respectively.

本研究提出了一种基于多模态知识图谱嵌入和层次凝聚聚类的新框架，Literally Author Name Disambiguation (LAND)，用于解决学术知识图谱中作者名称消歧的问题，并在两个知识图谱数据集上展示了比基线方法高 8-14% 的性能。