# TempoQR: Temporal Question Reasoning over Knowledge Graphs

arXiv: https://arxiv.org/abs/2112.05785 · HTML: https://mlsanity.com/paper/2112.05785/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 33 references that are in the map.
- First posted on arXiv 10 Dec 2021.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [ERNIE: Enhanced Language Representation with Informative Entities](https://mlsanity.com/paper/1905.07129/) (2019)
- [Representation Learning for Dynamic Graphs: A Survey](https://mlsanity.com/paper/1905.11485/) (2019)
- [Key-Value Memory Networks for Directly Reading Documents](https://mlsanity.com/paper/1606.03126/) (2016)
- [PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text](https://mlsanity.com/paper/1904.09537/) (2019)
- [Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text](https://mlsanity.com/paper/1809.00782/) (2018)
- [A Dataset for Answering Time-Sensitive Questions](https://mlsanity.com/paper/2108.06314/) (2021)
- [Tensor Decompositions for temporal knowledge base completion](https://mlsanity.com/paper/2004.04926/) (2020)
- [Improving Multi-hop Knowledge Base Question Answering by Learning Intermediate Supervision Signals](https://mlsanity.com/paper/2101.03737/) (2021)
- [Entities as Experts: Sparse Memory Access with Entity Supervision](https://mlsanity.com/paper/2004.07202/) (2020)
- [Question Answering Over Temporal Knowledge Graphs](https://mlsanity.com/paper/2106.01515/) (2021)
- [End-to-End Neural Entity Linking](https://mlsanity.com/paper/1808.07699/) (2018)
- [Knowledge Graph Completion via Complex Tensor Factorization](https://mlsanity.com/paper/1702.06879/) (2017)
- [Complex Temporal Question Answering on Knowledge Graphs](https://mlsanity.com/paper/2109.08935/) (2021)
- [Complex Knowledge Base Question Answering: A Survey](https://mlsanity.com/paper/2108.06688/) (2021)
- [An Interpretable Reasoning Network for Multi-Relation Question Answering](https://mlsanity.com/paper/1801.04726/) (2018)
- [TEQUILA: Temporal Question Answering over Knowledge Bases](https://mlsanity.com/paper/1908.03650/) (2019)
- [ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data](https://mlsanity.com/paper/2005.00792/) (2020)
- [Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader](https://mlsanity.com/paper/1905.07098/) (2019)
- [Event-QA: A Dataset for Event-Centric Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2004.11861/) (2020)
- [SYGMA: System for Generalizable Modular Question Answering OverKnowledge Bases](https://mlsanity.com/paper/2109.13430/) (2021)

## Built on by (most cited first, 26 of 26)

- [A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal](https://mlsanity.com/paper/2212.05767/) (2022)
- [Temporal Knowledge Graph Completion: A Survey](https://mlsanity.com/paper/2201.08236/) (2022)
- [Towards Benchmarking and Improving the Temporal Reasoning Capability of Large Language Models](https://mlsanity.com/paper/2306.08952/) (2023)
- [TempTabQA: Temporal Question Answering for Semi-Structured Tables](https://mlsanity.com/paper/2311.08002/) (2023)
- [Question Calibration and Multi-Hop Modeling for Temporal Question Answering](https://mlsanity.com/paper/2402.13188/) (2024)
- [Dynamic Benchmarking of Masked Language Models on Temporal Concept Drift with Multiple Views](https://mlsanity.com/paper/2302.12297/) (2023)
- [ReaRev: Adaptive Reasoning for Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2210.13650/) (2022)
- [TempRetriever: Fusion-based Temporal Dense Passage Retrieval for Time-Sensitive Questions](https://mlsanity.com/paper/2502.21024/) (2025)
- [A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects](https://mlsanity.com/paper/2308.02457/) (2023)
- [TrustUQA: A Trustful Framework for Unified Structured Data Question Answering](https://mlsanity.com/paper/2406.18916/) (2024)
- [TwiRGCN: Temporally Weighted Graph Convolution for Question Answering over Temporal Knowledge Graphs](https://mlsanity.com/paper/2210.06281/) (2022)
- [ForecastTKGQuestions: A Benchmark for Temporal Question Answering and Forecasting over Temporal Knowledge Graphs](https://mlsanity.com/paper/2208.06501/) (2022)
- [Temporal Knowledge Graph Question Answering: A Survey](https://mlsanity.com/paper/2406.14191/) (2024)
- [Enhancing Temporal Understanding in LLMs for Semi-structured Tables](https://mlsanity.com/paper/2407.16030/) (2024)
- [When Silence Is Golden: Can LLMs Learn to Abstain in Temporal QA and Beyond?](https://mlsanity.com/paper/2602.04755/) (2026)
- [Joint Multi-Facts Reasoning Network For Complex Temporal Question Answering Over Knowledge Graph](https://mlsanity.com/paper/2401.02212/) (2024)
- [HaSa: Hardness and Structure-Aware Contrastive Knowledge Graph Embedding](https://mlsanity.com/paper/2305.10563/) (2023)
- [Fusing Temporal Graphs into Transformers for Time-Sensitive Question Answering](https://mlsanity.com/paper/2310.19292/) (2023)
- [Discrete Minds in a Continuous World: Do Language Models Know Time Passes?](https://mlsanity.com/paper/2506.05790/) (2025)
- [Plan of Knowledge: Retrieval-Augmented Large Language Models for Temporal Knowledge Graph Question Answering](https://mlsanity.com/paper/2511.04072/) (2025)
- [Evaluating List Construction and Temporal Understanding capabilities of Large Language Models](https://mlsanity.com/paper/2506.21783/) (2025)
- [Leveraging Pre-trained Language Models for Time Interval Prediction in Text-Enhanced Temporal Knowledge Graphs](https://mlsanity.com/paper/2309.16357/) (2023)
- [Question Answering Over Spatio-Temporal Knowledge Graph](https://mlsanity.com/paper/2402.11542/) (2024)
- [Multi-view Attention Fusion of Heterogeneous Hypergraph with Dynamic Behavioral Profiling for Personalized Learning Resource Recommendation](https://mlsanity.com/paper/2603.13310/) (2026)
- [A Systematic Approach for Large Language Models Debugging](https://mlsanity.com/paper/2604.23027/) (2026)
- [SABET-QA: Temporal Knowledge Graph Question Answering](https://mlsanity.com/paper/2608.20083/) (2026)

## Abstract (arXiv)

Knowledge Graph Question Answering (KGQA) involves retrieving facts from a Knowledge Graph (KG) using natural language queries. A KG is a curated set of facts consisting of entities linked by relations. Certain facts include also temporal information forming a Temporal KG (TKG). Although many natural questions involve explicit or implicit time constraints, question answering (QA) over TKGs has been a relatively unexplored area. Existing solutions are mainly designed for simple temporal questions that can be answered directly by a single TKG fact. This paper puts forth a comprehensive embedding-based framework for answering complex questions over TKGs. Our method termed temporal question reasoning (TempoQR) exploits TKG embeddings to ground the question to the specific entities and time scope it refers to. It does so by augmenting the question embeddings with context, entity and time-aware information by employing three specialized modules. The first computes a textual representation of a given question, the second combines it with the entity embeddings for entities involved in the question, and the third generates question-specific time embeddings. Finally, a transformer-based encoder learns to fuse the generated temporal information with the question representation, which is used for answer predictions. Extensive experiments show that TempoQR improves accuracy by 25--45 percentage points on complex temporal questions over state-of-the-art approaches and it generalizes better to unseen question types.