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

2025-01-08

AI Summary

To address the scarcity and labor-intensive construction of temporal knowledge graph (TKG) question-answering (QA) data, this paper proposes the first fine-grained QA classification framework specifically designed for TKGs, systematically modeling question types via timeline-context semantic relations. We further introduce an end-to-end, TKG-agnostic controllable QA generation method that integrates temporal pattern mining, structured templates, and rule-guided strategies. Additionally, we release a lightweight, modular Python toolkit. Experiments demonstrate substantial improvements in both QA pair generation efficiency and lexical/semantic diversity. Our approach achieves high-quality, broad-coverage, and temporally consistent QA data across multiple benchmark TKGs—including ICEWS14, ICEWS05-15, and YAGO15k—thereby establishing a reusable data foundation and technical infrastructure for TKG-QA research.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Natural Language Processing: Question Answering
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Web Mining and Content Analysis: Community question answering
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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 extbf{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.

Problem

• Temporal Knowledge Graphs
• Data Sparsity
• Personalized Question Answering

Innovation

• TimelineKGQA
• Automatic Question Generation
• Open-source Code