@inproceedings{gong-etal-2025-rtqa,
    title = "{RTQA} : Recursive Thinking for Complex Temporal Knowledge Graph Question Answering with Large Language Models",
    author = "Gong, Zhaoyan  and
      Li, Juan  and
      Liu, Zhiqiang  and
      Liang, Lei  and
      Chen, Huajun  and
      Zhang, Wen",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.499/",
    pages = "9864--9881",
    ISBN = "979-8-89176-332-6",
    abstract = "Current temporal knowledge graph question answering (TKGQA) methods primarily focus on implicit temporal constraints, lacking the capability to handle more complex temporal queries, and struggle with limited reasoning abilities and error propagation in decomposition frameworks. We propose RTQA, a novel framework to address these challenges by enhancing reasoning over TKGs without requiring training. Following recursive thinking, RTQA recursively decomposes questions into sub-problems, solves them bottom-up using LLMs and TKG knowledge, and employs multi-path answer aggregation to improve fault tolerance. RTQA consists of three core components: the Temporal Question Decomposer, the Recursive Solver, and the Answer Aggregator. Experiments on MultiTQ and TimelineKGQA benchmarks demonstrate significant Hits@1 improvements in ``Multiple'' and ``Complex'' categories, outperforming state-of-the-art methods. Our code and data are available at https://github.com/zjukg/RTQA."
}