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。