TimelineKGQA: A Comprehensive Question-Answer Pair Generator for Temporal Knowledge Graphs
This addresses the need for better datasets and tools for researchers and developers working on question answering over temporal knowledge graphs, though it appears incremental as it builds on existing TKG frameworks.
The paper tackled the problem of limited datasets and difficulties in generating custom question-answering pairs for temporal knowledge graphs by proposing TimelineKGQA, a universal temporal QA generator applicable to any TKGs, resulting in a tool released as an open-source Python package.
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.