The Scholarly Hybrid Question Answering over Linked Data (QALD) Challenge at the International Semantic Web Conference (ISWC) 2024 focuses on Question Answering (QA) over diverse scholarly sources: DBLP, SemOpenAlex, and Wikipedia-based texts. This paper describes a methodology that combines SPARQL queries, divide and conquer algorithms, and a pre-trained extractive question answering model. It starts with SPARQL queries to gather data, then applies divide and conquer to manage various question types and sources, and uses the model to handle personal author questions. The approach, evaluated with Exact Match and F-score metrics, shows promise for improving QA accuracy and efficiency in scholarly contexts.

翻译：2024年国际语义网会议（ISWC）的学术混合链接数据问答（QALD）挑战赛聚焦于跨多源学术数据（包括DBLP、SemOpenAlex和基于维基百科的文本）的问答任务。本文提出了一种结合SPARQL查询、分治算法与预训练抽取式问答模型的方法论。该方法首先通过SPARQL查询收集数据，继而运用分治策略处理不同问题类型与数据源，最后采用预训练模型处理个人作者类问题。经精确匹配与F值指标评估，该方法在提升学术领域问答准确性与效率方面展现出良好潜力。