RTQA : Recursive Thinking for Complex Temporal Knowledge Graph Question Answering with Large Language Model

ACL ARR 2025 May Submission5273 Authors

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.

Paper Type: Long

Research Area: Question Answering

Research Area Keywords: knowledge base QA

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 5273