# A Benchmark for Generalizable and Interpretable Temporal Question Answering over Knowledge Bases

arXiv: https://arxiv.org/abs/2201.05793 · HTML: https://mlsanity.com/paper/2201.05793/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 15 Jan 2022.

## Builds on (most cited first, 7 of 7)

- [The Web as a Knowledge-base for Answering Complex Questions](https://mlsanity.com/paper/1803.06643/) (2018)
- [Large-scale Simple Question Answering with Memory Networks](https://mlsanity.com/paper/1506.02075/) (2015)
- [Question Answering Over Temporal Knowledge Graphs](https://mlsanity.com/paper/2106.01515/) (2021)
- [Complex Temporal Question Answering on Knowledge Graphs](https://mlsanity.com/paper/2109.08935/) (2021)
- [TEQUILA: Temporal Question Answering over Knowledge Bases](https://mlsanity.com/paper/1908.03650/) (2019)
- [Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering](https://mlsanity.com/paper/1808.04126/) (2018)
- [SYGMA: System for Generalizable Modular Question Answering OverKnowledge Bases](https://mlsanity.com/paper/2109.13430/) (2021)

## Built on by (most cited first, 9 of 9)

- [TempTabQA: Temporal Question Answering for Semi-Structured Tables](https://mlsanity.com/paper/2311.08002/) (2023)
- [Carpe Diem: On the Evaluation of World Knowledge in Lifelong Language Models](https://mlsanity.com/paper/2311.08106/) (2023)
- [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)
- [NOVA: NOise-aware Verbal Confidence CAlibration for Robust Large Language Models in RAG Systems](https://mlsanity.com/paper/2601.11004/) (2026)
- [LLM-Symbolic Integration for Robust Temporal Tabular Reasoning](https://mlsanity.com/paper/2506.05746/) (2025)
- [Semantic Framework based Query Generation for Temporal Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2210.04490/) (2022)
- [TRAVELER: A Benchmark for Evaluating Temporal Reasoning across Vague, Implicit and Explicit References](https://mlsanity.com/paper/2505.01325/) (2025)
- [Evidence-Guided Schema Normalization for Temporal Tabular Reasoning](https://mlsanity.com/paper/2512.00329/) (2025)

## Abstract (arXiv)

Knowledge Base Question Answering (KBQA) tasks that involve complex reasoning are emerging as an important research direction. However, most existing KBQA datasets focus primarily on generic multi-hop reasoning over explicit facts, largely ignoring other reasoning types such as temporal, spatial, and taxonomic reasoning. In this paper, we present a benchmark dataset for temporal reasoning, TempQA-WD, to encourage research in extending the present approaches to target a more challenging set of complex reasoning tasks. Specifically, our benchmark is a temporal question answering dataset with the following advantages: (a) it is based on Wikidata, which is the most frequently curated, openly available knowledge base, (b) it includes intermediate sparql queries to facilitate the evaluation of semantic parsing based approaches for KBQA, and (c) it generalizes to multiple knowledge bases: Freebase and Wikidata. The TempQA-WD dataset is available at https://github.com/IBM/tempqa-wd.