构建时间知识图谱问答生成框架，解决数据稀缺难题
TimelineKGQA: A Comprehensive Question-Answer Pair Generator for Temporal Knowledge Graphs
- 按时间上下文关系对问题分类，实现精准生成
- 支持任意时间知识图谱的通用问答对生成
- 开源工具包，适合研究者快速构建数据集
面向时间知识图谱（TKG）的问答任务对于理解动态事实与关系至关重要，但其发展受限于数据集匮乏及自定义问答对生成困难。本文提出一种基于时间线-上下文关系的新分类框架，并构建了名为TimelineKGQA的通用时间问答生成器，可适用于任意时间知识图谱。该工具以开源Python包形式发布，便于研究者高效构建高质量问答数据集。
原文摘要 · Abstract (English)
Question answering over temporal knowledge graphs (TKGs) is crucial for understanding evolving facts and relationships, yet its development is hindered by limited datasets and difficulties in generating custom QA pairs. We propose a novel categorization framework based on timeline-context relationships, along with \textbf{TimelineKGQA}, a universal temporal QA generator applicable to any TKGs. The code is available at: \url{https://github.com/PascalSun/TimelineKGQA} as an open source Python package.
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