# TimelineKGQA: A Comprehensive Question-Answer Pair Generator for Temporal Knowledge Graphs

Source: [TimelineKGQA: A Comprehensive Question-Answer Pair Generator for Temporal Knowledge Graphs](<https://www.sota2.com/research/paper/68fa768e1e209131cea6dafd>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:56:26.552Z

Authors: Qiang Sun, Sirui Li, Du Huynh, Mark Reynolds, Wei Liu

Published: 2025-01-08T08:30:44.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2501.04343>)

[PDF](<https://arxiv.org/pdf/2501.04343.pdf>)