Question Answering over Temporal Knowledge Graphs (TKGQA) requires reasoning over time-sensitive facts, yet existing embedding-based methods struggle with multi-step queries due to single-pass reasoning pipelines. We propose SABET-QA, a framework that iteratively refines reasoning states across multiple hops via a bidirectional entity-temporal scoring mechanism and a slot-aware contextualization module that aligns question semantics with temporal KG embeddings.
arXiv:2607. 14770v1 Announce Type: new Abstract: Knowledge graph question generation (KGQG) aims to generate natural-language questions from structured graph evidence.
By Xuemeng Liu, Zhengpin Li, Wanpeng Tang, Haotong Xie, Wentao Zhang
arXiv:2609.22213v1 Announce Type: new
Abstract: Temporal Knowledge Graph Question Answering (TKGQA) requires answer inference from evidence that is both structurally valid and temporally admissible....
By Xiaokun Guo, Zhen Xu, Dongdong Huo, Yanqiu Zhang, Dongjin Yu, Yu Wang
arXiv:2609.14528v1 Announce Type: cross
Abstract: Multi-Hop Knowledge Graph Question Answering (KGQA) tasks require models to assemble relational evidence along paths in a KG to answer natural-langua...
By Eduin E. Hernandez, Luis F. Garcia, Nurassyl Askar, Sergio A. Diaz, Stefano Rini
arXiv:2609.13457v1 Announce Type: new
Abstract: Timeseries multimodal large language models (TS-MLLMs) have recently begun leveraging the reasoning capabilities of large language models (LLMs) for qu...
By Sudarshan Regmi, Arvind Pillai, Yu Yvonne Wu, Yuliang Chen, Bibek Panthi, Tess Z. Griffin, Michael V. Heinz, Lisa Marsch, Nicholas C. Jacobson, Andrew Campbell
arXiv:2606. 00029v1 Announce Type: cross Abstract: Retrieval-augmented generation systems struggle with temporal reasoning and evidence fusion when answering complex questions over historical criminal case narratives.
By Sidra Nasir, Muhammad Noman Zahid, Rizwan Ahmed Khan
arXiv:2605. 07121v2 Announce Type: replace Abstract: Temporal knowledge graphs (TKGs) represent time-stamped relational facts and support a wide range of reasoning tasks over evolving events.
By Seunghan Lee, Jun Seo, Jaehoon Lee, Sungdong Yoo, Minjae Kim, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, SoonYoung Lee, Wonbin Ahn
arXiv:2607. 06527v1 Announce Type: cross Abstract: Multi-hop Question Answering over Knowledge Graphs faces a critical challenge: traditional retrieve-then-read pipelines break differentiability, preventing the retriever from learning to bridge the semantic gap where intermediate nodes lack lexical overlap with the query.
By Sambaran Bandyopadhyay, Ananth Muppidi
arXiv:2608. 16224v1 Announce Type: cross Abstract: By leveraging large-scale pretraining, LLMs can interpret diverse temporal expressions and question formulations without task-specific training.
By Xinlong Dai, Jinchuan Zhang, Lei Gao, Xinzhe Hu, Yuefeng He, Hui Gao
arXiv:2608. 04444v1 Announce Type: cross Abstract: Large language models (LLMs) often generate inaccurate answers due to their reliance on static internal knowledge.
By Jiaoyang Li, Junhao Ruan, Shengwei Tang, Kaiyan Chang, Zhengtao Yu, Tong Xiao, Jingbo Zhu
arXiv:2602. 15353v3 Announce Type: replace-cross Abstract: Large pretrained language models and neural reasoning systems have advanced many natural language tasks, yet they remain challenged by knowledge-intensive queries that require precise, structured multi-hop inference.
By Rong Fu, Yang Li, Zeyu Zhang, Jiekai Wu, Yaohua Liu, Shuaishuai Cao, Yangchen Zeng, Yuhang Zhang, Xiaojing Du, Simon Fong
arXiv:2607. 25947v1 Announce Type: new Abstract: Question answering (QA) over irregular clinical time series (ICTS) plays a pivotal role in a wide range of healthcare applications.
By Frank Nie, Ethan B Liu, Yuan Zhu, Wei Fan, Jindong Han