TCC-RAG: Temporal Causal Chain-Guided RAG for Temporal Knowledge Graph Question Answering

Xinxun Zhang; Yuxiang Wang; Shanshan Feng; Jun Zhang; Tieyun Qian; Binhang Yuan; Kai Yin; Xiao Yan

cs.CLOct 28, 2026

TCC-RAG: Temporal Causal Chain-Guided RAG for Temporal Knowledge Graph Question Answering

Authors: Xinxun Zhang, Yuxiang Wang, Shanshan Feng, Jun Zhang, Tieyun Qian, Binhang Yuan, Kai Yin, Xiao Yan,

Organizations: School of Computer Science, Wuhan University · Tencent Inc. · Hong Kong University of Science and Technology · School of Computer Science, Central China Normal University

Abstract

Recent advances in large language models (LLMs) have spurred growing interest in temporal knowledge graph question answering (TKGQA). Most existing methods rely on semantic retrieval augmented generation (RAG), which suffers from fundamental limitations in complex temporal reasoning. On the one hand, semantic-based RAG methods struggle to capture the multi-hop causal knowledge required for complex temporal reasoning. On the other hand, these methods introduce excessive redundant knowledge, trapping LLMs in spurious reasoning chains that potentially result in erroneous answers. To address these problems, we propose a Temporal Causal Chain-guided RAG (TCC-RAG) to retrieve a concise yet sufficient set of causal knowledge to support complex temporal reasoning. Specifically, we reformulate the temporal knowledge graph into a multi-view causal cue graph, with causal cues inducing connections that explicitly encode temporal evolution and causal dynamics. Based on the causal cue graph, we construct a query-specific temporal causal chain to guide the retrieval process, enabling the model to focus on a concise set of causally relevant knowledge. Finally, we introduce a causal evidence filter to refine the retrieved knowledge, ensuring its causal consistency with the underlying temporal logic of the query. Experiments show that TCC-RAG achieves substantial performance gains of 10.72% and 8.40% on two TKGQA benchmarks, and delivers a remarkable 62.97% improvement for multi-step temporal reasoning.

Figures & tables

Fig. 1: The limitations of semantic-based RAG methods and design of our temporal causal chain-guided RAG approach.

Fig. 2: The framework of TCC-RAG. (a) Temporal facts are organized into a Temporal Event Graph (TEG) and two temporally sorted indices: the Subject Causal Cue Index (SCCI) and Object Causal Cue Index (OCCI). (b) NER and LLM extraction ground query entities through matching and consistency checking. Relation, cue, and role parsing then guide view routing and temporal chain retrieval. (c) The Causal Evidence Filter removes equivalent duplicates and selects a sufficient subset of query-consistent facts. (d) The LLM reasons over the filtered evidence to generate answers. When structured retrieval yields no facts, semantic retrieval provides fallback candidates that also pass through the filter.

Structure	Storage	Construction	Per-query
TEG ( $\mathcal{G}^{t}$ )	$\mathcal{O}(\|\mathcal{F}\|)$	$\mathcal{O}(\|\mathcal{F}\|)$	$\mathcal{O}(\mathrm{deg})$
SCCI ( $\mathcal{D}^{s}$ )	$\mathcal{O}(\|\mathcal{F}\|)$	$\mathcal{O}(\|\mathcal{F}\|\log d_{\max})$	$\mathcal{O}(\log d_{\max}{+}k)$
OCCI ( $\mathcal{D}^{o}$ )	$\mathcal{O}(\|\mathcal{F}\|)$	$\mathcal{O}(\|\mathcal{F}\|\log d_{\max})$	$\mathcal{O}(\log d_{\max}{+}k)$

TABLE I: Complexity of the multi-view causal cue graph.

|\mathcal{F}|

: total facts;

d_{\max}

: maximum posting list length;

k

: number of cue-consistent events per query. Per-query measures index retrieval with grounded keys and a resolved temporal boundary (see Section IV-E ).

Panel of Figure 4 — Fig. 3: The prompt template for causal chain-guided temporal reasoning based answer generation.

Category		Train	Dev	Test
Single	Equal	135,890	18,983	17,311
	Before/After	75,340	11,655	11,073
	First/Last	72,252	11,097	10,480
Multiple	Equal Multi	16,893	3,213	3,207
	After First	43,305	6,499	6,266
	Before Last	43,107	6,532	6,247

TABLE II: Statistics of question categories in MULTITQ.

Category	Train	Dev	Test
Explicit	2,724	1,302	1,311
Implicit	651	291	292
Temporal	2,657	1,073	1,067
Ordinal	938	570	567
Total	6,970	3,236	3,237

TABLE III: Statistics of question categories in TimeQuestions.

Model	Hits@1					Hits@10
	Overall	Question Type		Answer Type		Overall	Question Type		Answer Type
		Multiple	Single	Entity	Time		Multiple	Single	Entity	Time
BERT	0.083	0.061	0.092	0.101	0.040	0.441	0.392	0.461	0.531	0.222
DistilBERT	0.083	0.074	0.087	0.102	0.037	0.482	0.426	0.505	0.591	0.216
ALBERT	0.108	0.086	0.116	0.139	0.032	0.484	0.415	0.512	0.589	0.228
EmbedKGQA	0.206	0.134	0.235	0.290	0.001	0.459	0.439	0.467	0.648	0.001

TABLE IV: Results of TCC-RAG and baselines on Hits@1 and Hits@10 on MULTITQ. The best and second best results are marked in bold and underlined, respectively. The bottom row is the relative improvement of TCC-RAG over the best baseline.

Model	Overall	Multiple	Single	Entity	Time
w/o CCG	0.647	0.127	0.837	0.517	0.933
w/o PQP	0.778	0.653	0.824	0.759	0.821
w/o CEF	0.841	0.664	0.910	0.791	0.958
TCC-RAG	0.847	0.691	0.911	0.801	0.959

TABLE VI: Ablation studies (Hits@1) of TCC-RAG on MULTITQ.

Fig. 4: A comparison of the retrieved facts between semantic-based RAG methods and TCC-RAG on MULTITQ. Facts highlighted in red are causally essential evidence.

Dataset	Path	Coverage	Hits@1
MULTITQ	Structured	84.46%	0.912
MULTITQ	Fallback	15.54%	0.492
TimeQuestions	Structured	91.82%	0.738
TimeQuestions	Fallback	8.18%	0.398

TABLE VII: Retrieval-path coverage and conditional Hits@1.

Fig. 5: Experiment results of Hits@1 across multiple temporal granularities on the MULTITQ dataset.

Fig. 6: Comparison of token cost and inference time between TCC-RAG and RTQA on MULTITQ.

Knowledge Graph Question Answering

Knowledge Graph Question Answering answers natural-language questions by finding and reasoning over entities and relations in structured knowledge graphs.

See all 169 papers

Let the Agent Search: Autonomous Exploration Beats Rigid Workflows in Temporal Question Answering
Mar 2, 2026Xufei Lv, Jiahui Yang, Haoyuan Sun +5Temporal QA Knowledge Graph Question Answering
Tsinghua University · National University of Defense Technology
IA-RAG: Interval-Algebra-Driven Temporal Reasoning for Dynamic Knowledge Retrieval
Jun 4, 2026Xiaoman Wang, Yaoze Zhang, Wenzhuo Fan +7Retrieval-Augmented Generation Temporal IR
East China Normal University · Shanghai Artificial Intelligence Laboratory · University of Shanghai for Science and Technology +1
SABET-QA: Temporal Knowledge Graph Question Answering
Aug 20, 2026Brahim Touayouch, Mirette Moawad, Dmitry AkulovKnowledge Graph Question Answering Temporal QA
QuickSort Research, Paris, France · ENS Paris-Saclay, École Polytechnique, France

Mar 2, 2026J/K move · Enter open · S save

Let the Agent Search: Autonomous Exploration Beats Rigid Workflows in Temporal Question Answering

Xufei Lv, Jiahui Yang, Haoyuan Sun, Xialin Su+4

Tsinghua University · National University of Defense Technology

Temporal QA Knowledge Graph Question Answering Temporal KGs LLM Reasoning with Graphs LLM Agents

Open paper AskPDF arXiv

Temporal Knowledge Graph Question Answering (TKGQA) is challenging because it requires multi-hop reasoning under complex temporal constraints. Recent LLM-based approaches have improved semantic modeling for this task, but many still rely on fixed reasoning workflows or costly post-training, which can limit adaptability and make error recovery difficult. We show that enabling an off-the-shelf Large Language Model (LLM) to determine its next action is already effective in a zero-shot setting. Based on this insight, we propose AT2QA, an Autonomous and Training-free Agent for TKG Question Answering. AT2QA empowers the LLM to iteratively interact with the TKG via a generic search tool, inherently enabling autonomous exploration and dynamic self-correction during reasoning. To further elicit the LLM's potential for complex temporal reasoning, we introduce a training-free experience mining mechanism that distills a compact few-shot demonstration library from successful self-generated trajectories. AT2QA also yields a transparent audit trail for every prediction. Experiments on three challenging benchmarks -- MultiTQ, Timeline-CronQuestion, and Timeline-ICEWS-Actor -- show that AT2QA achieves new state-of-the-art performance, surpassing the strongest baselines by 10.7, 4.9, and 11.2 absolute points, respectively. Our code is available at https://github.com/AT2QA-Official-Code/AT2QA-Official-Code

Abstract

Knowledge Graph Question Answering

Explore similar work

Let the Agent Search: Autonomous Exploration Beats Rigid Workflows in Temporal Question Answering

IA-RAG: Interval-Algebra-Driven Temporal Reasoning for Dynamic Knowledge Retrieval

SABET-QA: Temporal Knowledge Graph Question Answering