TimelineKGQA: A Comprehensive Question-Answer Pair Generator for Temporal Knowledge Graphs
Qiang Sun, Sirui Li, Du Huynh, Mark Reynolds, Wei Liu
TL;DR
TimelineKGQA is a universal tool for generating question-answer pairs over temporal knowledge graphs, addressing dataset limitations and enabling better temporal reasoning in QA systems.
Contribution
It introduces a new categorization framework and a versatile QA generator applicable to any TKG, facilitating research and development.
Findings
Provides a comprehensive QA dataset for TKGs
Enables custom question generation for evolving facts
Improves temporal reasoning in QA systems
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.
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Taxonomy
TopicsSemantic Web and Ontologies · Topic Modeling · Data Quality and Management