Papers with MultiTQ
RTQA : Recursive Thinking for Complex Temporal Knowledge Graph Question Answering with Large Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Current temporal knowledge graph question answering methods focus on implicit temporal constraints and lack the capability to handle complex temporal queries. |
| Approach: | They propose a temporal knowledge graph question answering framework that recursively decomposes questions into sub-problems and employs multi-path answer aggregation to improve fault tolerance. |
| Outcome: | The proposed framework outperforms existing methods on multiTQ and TimelineKGQA benchmarks. |
Multi-granularity Temporal Question Answering over Knowledge Graphs (2023.acl-long)
Copied to clipboard
| Challenge: | Existing work on temporal knowledge graphs ignores fact that real-life applications of TKGQA are complex in temporal granularity. |
| Approach: | They propose a large scale dataset for multi-granularity temporal question answering over knowledge graphs . they propose comparing MultiQA over MultiTQ to better reflect real-world challenges . |
| Outcome: | The proposed dataset is among the first of its kind and features multiple temporal granularities. |
Temp-R1: A Unified Autonomous Agent for Complex Temporal KGQA via Reverse Curriculum Reinforcement Learning (2026.acl-long)
Copied to clipboard
Zhaoyan Gong, Zhiqiang Liu, Songze Li, Xiaoke Guo, Yuanxiang Liu, Xinle Deng, Zhizhen Liu, Lei Liang, Huajun Chen, Wen Zhang
| Challenge: | Existing methods rely on fixed workflows and expensive closed-source APIs, limiting flexibility and scalability. |
| Approach: | They propose a temporal reasoning agent that trains on difficult questions first . they expand the action space with specialized internal actions alongside external action . |
| Outcome: | The proposed agent improves 19.8% over baselines on complex questions and multi-tasks. |
Regret-Now: A Physics-Inspired Regret Framework for Temporal Knowledge Graph Question Answering with LLMs (2026.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models have impressive results in general reasoning tasks, but they still exhibit a lack of dynamic error-correction. |
| Approach: | They propose a temporal reasoning framework that uses the principle of minimum potential energy to model the reasoning process as a dynamic trajectory moving toward a more stable state. |
| Outcome: | The proposed framework shows consistent gains over strong baselines on two standard TKGQA benchmarks. |
Self-Improvement Programming for Temporal Knowledge Graph Question Answering (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods implicitly model time constraints by learning time-aware embeddings of questions and candidate answers, which is far from understanding the question comprehensively. |
| Approach: | They propose a temporal-based temporal programming method that leverages the in-context learning ability of Large Language Models to understand combinatory time constraints in questions. |
| Outcome: | The proposed method outperforms existing methods on multiTQ and CronQuestions datasets and is highly efficient on multi-level questions. |
Temporal Evidence Chain for Temporal Knowledge Graph Question Answering with Large Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Temporal Knowledge Graph Question Answering (TKGQA) aims to answer temporal questions using knowledge from Temporal knowledge graphs. |
| Approach: | They propose a framework to construct temporal evidence chains for LLM reasoning using Temporal Knowledge Graphs. |
| Outcome: | TECQA outperforms existing methods on MultiTQ and CronQuestions. |