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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Bounded Path Context: A Controlled Study of Visible Path History in LLM-Based Knowledge Graph Question Answering
May 26, 2026Xihang Shan, Ye LuoMulti-Hop QA LLM Reasoning with Graphs
School of Mathematical Sciences Xiamen University · School of Informatics Xiamen University
Peak-Then-Collapse and the Four Interface Channels of Knowledge-Graph Tool Use
May 25, 2026Tianda Sun, Dimitar KazakovTool-Use Evaluation Reinforcement Learning
University of York · Department of Computer Science · Heslington, York +1
Knowledge Graph-Driven Expert-Level Reasoning for Neuroscience
May 24, 2026Jake Stephen, Niraj K. JhaKnowledge Augmentation for Language Models LLM Reasoning with Graphs
Department of Electrical and Computer Engineering, Princeton University
SeedER: Seed-Expand-Retrieve for Efficient Knowledge Graph Retrieval
May 22, 2026Hamed Shirzad, Frederik Wenkel, Dominique Beaini +2Graph-Based Retrieval Knowledge Graph Question Answering
Valence Labs, Montréal, QC, Canada · Recursion, Salt Lake City, UT, USA · University of British Columbia, Department of Computer Science, Vancouver, BC, Canada
Ex-GraphRAG: Interpretable Evidence Routing for Graph-Augmented LLMs
May 21, 2026Yoav Kor Sade, Arvindh Arun, Rishi Puri +2Graph Neural Networks LLM Reasoning with Graphs
Tel Aviv University · Institute for AI, University of Stuttgart · NVIDIA +1
PersonalAI 2.0: Enhancing knowledge graph traversal/retrieval with planning mechanism for Personalized LLM Agents
May 13, 2026Mikhail Menschikov, Matvey Iskornev, Alexander Kharitonov +8Multi-Hop QA Graph Retrieval-Augmented Generation
Skoltech, Moscow, Russia · SberAI, Moscow, Russia · Huawei, Moscow, Russia +4
CommonWhy: A Dataset for Evaluating Entity-Based Causal Commonsense Reasoning in Large Language Models
May 13, 2026Armin Toroghi, Faeze Moradi Kalarde, Scott SannerAbductive Reasoning Causal Reasoning in Language Models
University of Toronto · Toronto, ON, Canada
ClinicalBench: Stress-Testing Assertion-Aware Retrieval for Cross-Admission Clinical QA on MIMIC-IV
May 11, 2026Alex StinardHealthcare Clinical QA
Department of Clinical Sciences, College of Medicine · University of Central Florida, Orlando, FL 32816
PathISE: Learning Informative Path Supervision for Knowledge Graph Question Answering
May 11, 2026Shengxiang Gao, Chao Lei, Jey Han Lau +1LLM Grounding Graph Retrieval-Augmented Generation
The University of Melbourne
PrimeKG-CL: A Continual Graph Learning Benchmark on Evolving Biomedical Knowledge Graphs
May 11, 2026Yousef A. Radwan, Yao Li, Qing Qing +5KG Embedding Knowledge Graph Question Answering
Technology, Innovation, Entrepreneurship Department King Abdullah University of Science and Technology · School of Computing and Information Systems The University of Melbourne · College of Computer Science and Technology Jilin University +3
LLM-Guided Monte Carlo Tree Search over Knowledge Graphs: Composing Mechanistic Explanations for Drug-Disease Pairs
May 10, 2026Rishabh Jakhar, Michel Dumontier, Remzi CelebiLLM Reasoning with Graphs Scientific Discovery
Institute of Data Science, Department of Advanced Computing Sciences, Maastricht University, The Netherlands
HOME-KGQA: A Benchmark Dataset for Multimodal Knowledge Graph Question Answering on Household Daily Activities
May 10, 2026Shusaku Egami, Aoi Ohta, Tomoki Tsujimura +5Multi-Hop QA Spatiotemporal Reasoning
National Institute of Advanced Industrial Science and Technology (AIST) Koto-ku, Tokyo, Japan
Conformal Path Reasoning: Trustworthy Knowledge Graph Question Answering via Path-Level Calibration
May 8, 2026Shuhang Lin, Chuhao Zhou, Xiao Lin +5Conformal Prediction Knowledge Graph Question Answering
Rutgers University · Independent Researcher · University of Illinois Urbana-Champaign +1
SCPRM: A Schema-aware Cumulative Process Reward Model for Knowledge Graph Question Answering
May 4, 2026Jiujiu Chen, Yazheng Liu, Sihong Xie +1LLM Reasoning with Graphs Process Reward Models
HKUST (GZ)
Knowledge Graph Representations for LLM-Based Policy Compliance Reasoning
Apr 30, 2026Wilder Baldwin, Sepideh GhanavatiKG Construction Agentic RAG
University of Maine
CacheRAG: A Semantic Caching System for Retrieval-Augmented Generation in Knowledge Graph Question Answering
Apr 28, 2026Yushi Sun, Lei ChenRetrieval-Augmented Generation Graph Retrieval-Augmented Generation
HKUST Hong Kong, China · HKUST(GZ) / HKUST Guangzhou / Hong Kong, China
BioGraphletQA: Knowledge-Anchored Generation of Complex QA Datasets
Apr 28, 2026Richard A. A. Jonker, Bárbara Maria Ribeiro de Abreu Martins, Sérgio MatosBiomedical QA Synthetic Benchmark Generation
IEETA, DETI, LASI, University of Aveiro, Aveiro, Portugal · USF Atlântico Norte, Portugal
IndustryAssetEQA: A Neurosymbolic Operational Intelligence System for Embodied Question Answering in Industrial Asset Maintenance
Apr 25, 2026Chathurangi Shyalika, Dhaval Patel, Amit ShethEmbodied QA Predictive Maintenance
Artificial Intelligence Institute, University of South Carolina USA · Software Innovation Lab IBM Yorktown USA · Artificial Intelligence Institute, University of South Carolina Indian AI Research Organization
STEM: Structure-Tracing Evidence Mining for Knowledge Graphs-Driven Retrieval-Augmented Generation
Apr 24, 2026Peng Yu, En Xu, Bin Chen +2Graph Neural Networks Graph Retrieval-Augmented Generation
AI Product Center, Kingsoft Corporation, Beijing, China
GRISP: Guided Recurrent IRI Selection over SPARQL Skeletons
Apr 22, 2026Sebastian Walter, Hannah BastKnowledge Graph Question Answering Knowledge Graphs
University of Freiburg · Department of Computer Science · Freiburg im Breisgau, Germany
LogosKG: Hardware-Optimized Scalable and Interpretable Knowledge Graph Retrieval
Apr 20, 2026He Cheng, Yifu Wu, Saksham Khatwani +5Graph-Based Retrieval Knowledge Graph Question Answering
LARK Lab, University of Colorado Anschutz · University of Colorado Boulder · Loyola University Chicago +3
RLM-on-KG: Heuristics First, LLMs When Needed: Adaptive Retrieval Control over Mention Graphs for Scattered Evidence
Apr 18, 2026Andrea Volpini, Elie RaadMulti-Hop QA LLM Reasoning with Graphs
WordLift
Which bird does not have wings: Negative-constrained KGQA with Schema-guided Semantic Matching and Self-directed Refinement
Apr 16, 2026Midan Shim, Seokju Hwang, Kaehyun Um +1Knowledge Graph Question Answering
Department of Computer Science, Yonsei University
UltRAG: a Universal Simple Scalable Recipe for Knowledge Graph RAG
Jan 28, 2026Dobrik Georgiev, Kheeran K. Naidu, Alberto Cattaneo +3Multi-Hop QA LLM Reasoning with Graphs
Graphcore Research
SPARQL-LLM: Real-Time SPARQL Query Generation from Natural Language Questions
Dec 16, 2025Panayiotis Smeros, Vincent Emonet, Ruijie Wang +2Knowledge Graph Question Answering Natural Language to SPARQL
SIB Swiss Institute of Bioinformatics, Switzerland · SIB Swiss Institute of Bioinformatics and University of Zurich, Switzerland
KBQA-R1: Reinforcing Large Language Models for Knowledge Base Question Answering
Dec 10, 2025Xin Sun, Zhongqi Chen, Xing Zheng +6LLM Grounding RL for Language Model Reasoning
University of Science and Technology of China, Hefei, China · Independent · Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Ground-Truth Subgraphs for Better Training and Evaluation of Knowledge Graph Augmented LLMs
Nov 6, 2025Alberto Cattaneo, Carlo Luschi, Daniel JustusGraph Retrieval-Augmented Generation Synthetic Benchmark Generation
Graphcore Research
KGFR: A Foundation Retriever for Generalized Knowledge Graph Question Answering
Nov 6, 2025Yuanning Cui, Zequn Sun, Wei Hu +1Multi-Hop QA LLM Grounding
School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China · State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China · National Institute of Healthcare Data Science, Nanjing University, Nanjing 210093, China +2
CQD-SHAP: Explainable Complex Query Answering via Shapley Values
Oct 17, 2025Parsa Abbasi, Stefan HeindorfNeuro-Symbolic Reasoning Knowledge Graph Question Answering
Department of Computer Science, Paderborn University, Germany
Ontology-Guided Reverse Thinking Makes Large Language Models Stronger on Knowledge Graph Question Answering
Feb 17, 2025Runxuan Liu, Bei Luo, Jiaqi Li +5LLM Reasoning with Graphs Graph-Based Retrieval
Harbin Institute of Technology, Harbin, China · Beijing University of Posts and Telecommunications, Beijing, China · Joint Laboratory of HIT and iFLYTEK, Beijing, China +2

May 26, 2026, cs.CLJ/K move · Enter open · S save

Bounded Path Context: A Controlled Study of Visible Path History in LLM-Based Knowledge Graph Question Answering

Xihang Shan, Ye Luo

School of Mathematical Sciences Xiamen University · School of Informatics Xiamen University

Multi-Hop QA LLM Reasoning with Graphs Knowledge Graph Question Answering LLM Context Management Prompt Engineering

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LLM-based knowledge-graph question answering (KGQA) delegates graph traversal to language models, turning each question into a sequence of local relation-selection decisions repeated across beams and hops. A common but untested default is to serialize the complete partial path into every routing prompt, even though the controller already maintains this path as exact symbolic state. Bounded Path Context (BPC) decouples these two roles: the controller retains full paths in symbolic memory for answer extraction and audit, while the relation-selection prompt exposes only the question, the current entity, outgoing relation candidates, and at most the last K hops. A controlled sweep over K -- fixing graph neighborhoods, beam budget, depth, decoding, and answer-extraction format -- shows that bounded histories match or exceed full-history prompting on complete WebQSP and CWQ test sets with Qwen3.5-9B-AWQ: K=1 achieves 0.487 answer-set F1 on WebQSP versus 0.472 for full history, and K=0 reaches 0.287 on CWQ versus 0.274, with 9.7% and 12.1% fewer input tokens respectively. At the 4B scale, K=1 remains the strongest setting on both benchmarks. Per-example analysis reveals that 71-84% of examples are unaffected by history length, while the affected cases expose when prior hops disambiguate versus distract. These results suggest that path serialization length is better treated as a tunable interface variable than as a default assumption in LLM-based graph controllers.

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