Knowledge Graph Question Answering

Also known as KGQA

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12 papers in the last four weeks, up 140% on the four weeks before. 0.1% of all new papers.

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Foresight-over-Graph: Reasoning Beyond Local Horizons for Knowledge Base Question Answering
Oct 6, 2026Yang Hong, Yajun Yang, Xin Wang +2Multi-Hop QA LLM Reasoning with Graphs
Tianjin University, Tianjin, China · Beijing Jiaotong University, Beijing, China
Aligning Multimodal Patient Evidence with Biomedical Knowledge Graphs for Clinical LLMs
Oct 5, 2026Jiawen Du, Arshan Ali Khan, Chenhao Zhang +7KG Construction Graph-Based Retrieval
University of North Carolina at Chapel Hill · Siddaganga Institute of Technology · Foci Labs +1
Wikidata Search Traces: A Dataset for Training Knowledge Graph Search Agents
Oct 5, 2026Mohamed Chenene, Carlos Rosas-Hinostroza, Anastasia Stasenko +2Multi-Hop QA Knowledge Graph Question Answering
PleIAs · Lattice, ENS-PSL, CNRS, Sorbonne Nouvelle · Université Paris Dauphine-PSL +4
GraphCert: Bootstrap Agentic Graph Reasoning with Certified Evidence Rubrics
Sep 30, 2026Weiqi Jiang, Yuchen Ying, Rui Wang +5LLM Reasoning with Graphs RL for Language Model Reasoning
Zhejiang University, Hangzhou, China · Nanyang Technological University, Singapore · Hangzhou City University, Hangzhou, China
Concept-Grounded Attention: A Controlled Evaluation of Graph-Injected Attention, Temporal Versioning, and Epistemic Status
Sep 30, 2026Sachin Dev Duggal, Pradyumna Swarnalatha Ramanna, Alexandros VassiliadesLLM Grounding Temporal Reasoning in Language Models
SeKondBrain AI Labs, London, United Kingdom.
Neural Structural Reasoner: A Brain-inspired Architecture for Reasoning over Structured Knowledge
Sep 29, 2026Zixing Jia, Yuhang Pan, Ni JiLink Prediction Relational Reasoning
Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 102206, China · Chinese Institute for Brain Research, Beijing; Beijing, 102206, China · Beijing Key Laboratory of Brain Science and Brain-Machine Interface +1
GeoOutageBench: Benchmarking Ambiguity-aware, Ontology-grounded Geospatiotemporal KGQA for Multimodal Power Outage and Resilience Analysis
Sep 28, 2026Ethan D. Frakes, Amy Kvien, Rishabh Kundu +8Knowledge Graph Question Answering Multimodal KGs
University of Central Florida Orlando, Florida, USA · Case Western Reserve University Cleveland, Ohio, USA
The Path Matters: Evaluating Small Language Models Beyond Answer Accuracy in KGQA
Sep 23, 2026Eduin E. Hernandez, Sergio A. Diaz, Luis F. Garcia +2Small Language Models Knowledge Graph Question Answering
NYCU, Taiwan · National Yang Ming Chiao Tung University (NYCU), Taiwan
UniDataAgent: An Ontology-Grounded Agent for Enterprise Question-to-Report Automation
Sep 23, 2026Yutai Duan, Yahui Zhao, Zhangti Li +5Tool-Using Agents LLM Agents
China Unicom Software Research Institute Beijing, China
Schema-Anchored Latent Reasoning for Semantic Parsing-Based Knowledge Base Question Answering
Sep 17, 2026Guangze Gao, Zixuan Li, Sikui Zhang +5LLM Grounding Knowledge Graph Question Answering
Institute of Automation, Chinese Academy of Sciences China · Institute of Computing Technology, Chinese Academy of Sciences China · University of Chinese Academy of Sciences China
Repair Before Reinforce: Context-Augmented Knowledge Graph Reasoning for Multi-Hop Question Answering
Sep 14, 2026Tharaka D. Fonseka, Niraj K. JhaMulti-Hop QA Knowledge Augmentation for Language Models
Dept. of Electrical and Computer Engineering, Princeton University
Cognition on Graph: Navigating Massive Knowledge Space via Cognitive Cycles and Bidirectional Graph-Text Synergy
Sep 14, 2026Gengxian Zhou, Jian Xu, Zichen Tang +3Multi-Hop QA Retrieval-Augmented Generation
Beijing University of Posts and Telecommunications · Zhongguancun Academy, Beijing · MAIS, Institute of Automation, Chinese Academy of Sciences +1
Enabling Knowledge Graph Understanding at Scale with the EXplore Your Graphs ENgine (EXYGEN)
Sep 12, 2026Harshdeep Singh, Yurui Zhu, Giovanni Colavizza +1Graph Retrieval-Augmented Generation Knowledge Graph Question Answering
Odoma Ltd.
The Answer Path and the Grounding Instruction in LLM Question Answering over Knowledge Graphs
Sep 9, 2026Arquimedes CanedoMulti-Hop QA LLM Grounding
Siemens Digital Industries Software Princeton, NJ, USA
OntoKG-EQ: A provenance-grounded, competency-question-governed knowledge graph for auditable analyst querying
Sep 8, 2026Furqan Nasir, Muhammad Atif Saeed, Muhammad Ehsan +2KG Construction Data Provenance
City University of Science and Information Technology (CUSIT), Peshawar, Pakistan · National University of Computer and Emerging Sciences2026 (FAST-NUCES), Islamabad, Pakistan · University of Engineering and Technology (UET), Peshawar, Pakistan
AdaPath: Query-Adaptive Path-Finding via Path-Bank for Multi-Hop Implicit Biomedical KGQA
Aug 31, 2026Jun Hyeong Kim, Dongki Kim, Yinhua Piao +1Multi-Hop QA Biomedical QA
KAIST
Query-Side Attacks on GNN-Based KGQA: Tracing Failures from Entity Linking to Answer Generation
Aug 26, 2026Pankaj Kumar, Subhankar MishraGraph-Based Retrieval Adversarial Perturbations
National Institute of Science Education and Research · Homi Bhabha National Institute
A Storage-Retrieval Gap in Parametric Knowledge Graph Memory
Aug 26, 2026Martino M. L. Pulici, Cuong Xuan Chu, Evgeny Kharlamov +1Knowledge Graph Question Answering Retrieval-Augmented Generation
Bosch Center for Artificial Intelligence, Robert-Bosch-Campus 1, 71272 Renningen, Germany · LMU Munich, Institute for Informatics, Oettingenstraße 67, 80538 Munich, Germany · University of Oslo, Department of Informatics, Gaustadalléen 23 B, 0373 Oslo, Norway +1
ENTLORE: A Graph-Grounded Benchmark for Latent Organizational Reasoning in Enterprise Question Answering
Aug 11, 2026Akrin Zheng, Alexander Wu, Alaia LiuGraph Retrieval-Augmented Generation Knowledge Graph Question Answering
KGCaRe: Explainable Complex Conditional Question Answering using Automatic Knowledge Graph Construction and Context Retrieval with LLMs
Aug 10, 2026Ghanshyam Verma, Simanta Sarkar, Devishree Pillai +6KG Construction Graph Retrieval-Augmented Generation
Insight Research Ireland Centre for Data Analytics, Data Science Institute, University of Galway, IDA Business Park, Galway, H91 AEX4, Co. Galway, Ireland. · Fidelity Investments, Boston, Massachusetts, USA. · Fidelity Investments, Dublin, Co. Dublin, Ireland.
KGCache: Amortized Subgraph Retrieval for KG Reasoning with LLMs
Aug 8, 2026Uros Stanic, Changcheng Yuan, Sabuj Laskar +1Knowledge Graph Question Answering
Texas A&M University College Station, TX, USA
Towards Researcher Agents for Knowledge-Graph Question Answering
Aug 7, 2026Tommaso Soru, Abdulsobur OyewaleLLM Agent Self-Improvement Knowledge Graph Question Answering
1Liber AI Research, London, United Kingdom
CoEvoKG: Co-Evolving Knowledge Graphs with Self-Evolving Search Agents
Aug 3, 2026Zhaoyang Li, Zenghuang Fu, Qiuyuan Ai +6Knowledge Graph Question Answering Knowledge Graphs
Peking University · Institute of Automation, Chinese Academy of Sciences · Mininglamp Technology
The Graph Language: How Knowledge Graphs Speak to Large Language Models
Aug 2, 2026Giuseppe PirròLLM Grounding Knowledge Augmentation for Language Models
DeMaCS, University of Calabria
BEST-KAG: Enhancing Question Answering of Building Engineering Standards with Multimodal Knowledge Graph Modeling and Large Language Model
Aug 2, 2026Jia-Rui Lin, Junxi Guo, Keyin Chen +1Document Understanding KG Construction
Department of Civil Engineering, Tsinghua University, Beijing, China, 100084 · Key Laboratory of Digital Construction and Digital Twin, Ministry of Housing and Urban-Rural Development, Beijing, China, 100084
GLM-RAG: Graph Language Models for Graph-Based Retrieval-Augmented Generation
Jul 30, 2026Maya Arseven, Anette Frank, Beni Egressy +2LLM Reasoning with Graphs Graph Retrieval-Augmented Generation
Institute of Computational Linguistics, Heidelberg University · Aleph Alpha Research
GGC: Selective Query Correction for Reliable Text-to-SPARQL Generation
Jul 30, 2026Ziyi Yang, Thanh-Son Nguyen, Tuan Anh Nguyen +1LLM Self-Correction LLM Reliability
1Nanyang Technological University, Centre for Info. Sciences and Systems · Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), Singapore
Making Mathematical Knowledge Explainable, Accessible and Interoperable Through Large Language Model Integration
Jul 27, 2026Jan Range, Björn Schembera, Dominik GöddekeLLM Grounding Knowledge Representation
Stuttgart Center for Simulation Science (SC SimTech), University of Stuttgart · Institute of Applied Analysis and Numerical Simulation, University of Stuttgart
SEGRA: Structured Experience-Guided Graph Reasoning Agent for Gremlin Based Question Answering
Jul 21, 2026Saiyue Lyu, Mariam Dundua, Vishaal Kapoor +5LLM Reasoning with Graphs LLM Agent Skill Learning
University of British Columbia · Amazon
Search-on-Graph-R1: Training Large Language Models to Search Knowledge Graphs with Reinforcement Learning
Jul 20, 2026Jia Ao Sun, Hao Yu, Fengran Mo +4LLM Reasoning with Graphs RL for Language Model Reasoning
Université de Montréal · Mila – Québec AI Institute · McGill University +2

Oct 6, 2026, cs.CLJ/K move · Enter open · S save

Foresight-over-Graph: Reasoning Beyond Local Horizons for Knowledge Base Question Answering

Yang Hong, Yajun Yang, Xin Wang, Liping Jing+1

Tianjin University, Tianjin, China · Beijing Jiaotong University, Beijing, China

Multi-Hop QA LLM Reasoning with Graphs Graph-Based Retrieval Knowledge Graph Question Answering

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Large language models (LLMs) have demonstrated strong capabilities in question answering, yet they still frequently suffer from hallucinations on knowledge-intensive tasks. Knowledge graphs (KGs) provide LLMs with structured, interpretable, and updatable factual grounding, making them a promising external knowledge source for reliable reasoning. However, existing LLM-guided graph reasoning methods typically rely on hop-wise greedy or beam-style pruning during evidence retrieval. Such local decision processes are inherently myopic: evidence that appears weak near the source may become crucial only after deeper graph context is explored, causing answer-critical branches to be discarded prematurely and making the reasoning chain difficult to recover. To address this limitation, we propose Foresight-over-Graph (FoG), a foresight-aware evidence retrieval framework for knowledge base question answering (KBQA). FoG iteratively constructs a question-relevant evidence subgraph and uses far-to-near feedback to guide path exploration, and maintains a compact memory subgraph to support continued exploration. Extensive experiments on widely used KBQA benchmarks demonstrate that FoG achieves state-of-the-art performance, with a particularly large improvement of 16.58% in Hit on CWQ, while also reducing LLM calls and token usage. Our code is available at https://github.com/yhong7/FoG .

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