KG Completion

KG: Knowledge Graph

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2 papers in the last four weeks, down 80% on the four weeks before. 0.0% of all new papers.

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Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion
Jun 4, 2026Dongxiao He, Ruqiong Zhang, Zhizhi Yu +4Graph Neural Networks KG Completion
College of Intelligence and Computing, Tianjin University
Link Prediction or Perdition: the Seeds of Instability in Knowledge Graph Embeddings
Jun 2, 2026Guillaume Méroué, Fabien Gandon, Pierre MonninLink Prediction KG Embedding
Université Côte d’Azur, Inria, CNRS, I3S, France
Generating Graph-Like Logical Rules for Knowledge Graph Reasoning via Diffusion Models
May 29, 2026Haoxiang Cheng, Yunfei Wang, Chao Chen +5Knowledge Graphs KG Completion
Laboratory for Big Data and Decision, National University of Defense Technology · National Key Laboratory of Information Systems Engineering, National University of Defense Technology · Microsoft Corporation +2
Beyond Chunk-Local Extraction: Cross-Chunk Graph Augmentation for GraphRAG
May 27, 2026Jiaming Zhang, Yibo Zhao, Jing Yu +2Multi-Hop QA Graph Retrieval-Augmented Generation
School of Data Science and Engineering, East China Normal University
Boosting Knowledge Graph Foundation Models via Enhanced Negative Sampling
May 26, 2026Yinan Liu, Wenjin Xu, Zhiyuan Zha +2KG Embedding Graph Foundation Models
Northeastern University Shenyang, China
Generative Representation Learning on Hyper-relational Knowledge Graphs via Masked Discrete Diffusion
May 22, 2026Jaejun Lee, Seheon Kim, Joyce Jiyoung WhangGraph Representation Learning Knowledge Representation
School of Computing, KAIST, Daejeon, South Korea · Department of AI Computing, KAIST, Daejeon, South Korea
Leveraging Graph Structure in Seq2Seq Models for Knowledge Graph Link Prediction
May 18, 2026Luu Huu Phuc, Ratan Bahadur Thapa, Mojtaba Nayyeri +3Sequence Modeling KG Completion
Analytic Computing, KI, University of Stuttgart, Stuttgart, Germany · Bosch Center for Artificial Intelligence, Stuttgart, Germany · WAIS, University of Southampton, United Kingdom
Multimodal Cultural Heritage Knowledge Graph Extension with Language and Vision Models
May 17, 2026Yang Zhang, Nada Mimouni, Jean-Claude Moissinac +1KG Construction Cultural Heritage
Télécom Paris, Institut Polytechnique de Paris, France · Center for Studies and Research in Computer Science and Communication, CNAM · Center for Studies and Research in Computer Science and Communication, CNAM, France +1
ArtifactLinker: Linking Scientific Artifacts for Automatic State-of-the-Art Discovery
May 16, 2026Haofei Yu, Jiaxuan You, Peter Clark +2AI Agents for Scientific Discovery Link Prediction
University of Illinois Urbana-Champaign · Allen Institute for AI
KGPFN: Unlocking the Potential of Knowledge Graph Foundation Model via In-Context Learning
May 14, 2026Yisen Gao, Jiaxin Bai, Haoyu Huang +5Graph Neural Networks In-Context Learning
Department of Computer Science and Engineering, HKUST, Hong Kong, China · Department of Computer Science and Engineering, HKBU, Hong Kong, China
CORE: Cyclic Orthotope Relation Embedding for Knowledge Graph Completion
May 11, 2026Yingqi Zeng, Luying Wang, Huiling ZhuGraph Representation Learning KG Embedding
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
CMKL: Modality-Aware Continual Learning for Evolving Biomedical Knowledge Graphs
May 11, 2026Yousef A. Radwan, Yao Li, Qing Qing +5Continual Learning KG Embedding
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 +2
Relations Are Channels: Knowledge Graph Embedding via Kraus Decompositions
May 11, 2026Sayan Kumar ChakiGraph Representation Learning KG Embedding
Inria, Laboratoire Hubert Curien, Université Jean Monnet
Graphlets as Building Blocks for Structural Vocabulary in Knowledge Graph Foundation Models
May 7, 2026Kossi Amouzouvi, Robert Wardenga, Jens Lehmann +1Graph Representation Learning Graph Foundation Models
ScaDS.AI Dresden/Leipzig, Technische Universität Dresden, Dresden, Germany · alphaspeech - c/o alpha NT GmbH, Dresden, Germany · Amazon, Technische Universität Dresden, Dresden, Germany +1
In Data or Invisible: Toward a Better Digital Representation of Low-Resource Languages with Knowledge Graphs
May 7, 2026Ndeye-Emilie MbengueAnalogical Reasoning Low-Resource Language Processing
WIMMICS · Université Côte d’Azur, Inria, CNRS, I3S, Sophia-Antipolis, France
RADD: Retrieval-Augmented Discrete Diffusion for Multi-Modal Knowledge Graph Completion
Apr 28, 2026Guanglin Niu, Bo LiKG Embedding Multimodal KGs
School of Artificial Intelligence, Beihang University
Bridging the Long-Tail Gap: Robust Retrieval-Augmented Relation Completion via Multi-Stage Paraphrase Infusion
Apr 24, 2026Fahmida Alam, Mihai Surdeanu, Ellen RiloffRetrieval-Augmented Generation KG Completion
Department of Computer Science University of Arizona, USA
GS-Quant: Granular Semantic and Generative Structural Quantization for Knowledge Graph Completion
Apr 23, 2026Qizhuo Xie, Yunhui Liu, Yu Xing +4Graph Representation Learning KG Completion
State Key Laboratory for Novel Software Technology, Nanjing University, China
Revisiting Catastrophic Forgetting in Continual Knowledge Graph Embedding
Apr 21, 2026Gerard Pons, Carlos Escolano, Besim Bilalli +1Graph Representation Learning KG Embedding
Data-intensive Technologies and Knowledge Systems (DTAK), Department of Service and Information System Engineering, Universitat Politècnica de Catalunya, Barcelona, 08034, Catalonia, Spain · Intelligent Data Science and Artificial Intelligence Research Center (IDEAI), Department of Computer Science, Universitat Politècnica de Catalunya, Barcelona, 08034, Catalonia, Spain
One Pass for All: A Discrete Diffusion Model for Knowledge Graph Triple Set Prediction
Apr 20, 2026Jihong Guan, Jiaqi Wang, Wengen Li +3Link Prediction KG Completion
School of Computer Science and Technology, Tongji University, Shanghai, China · School of Computer Science, Fudan University, Shanghai, China
Hyper-modal Imputation Diffusion Embedding with Dual-Distillation for Federated Multimodal Knowledge Graph Completion
Jun 27, 2025Ying Zhang, Yu Zhao, Xuhui Sui +5Multimodal Federated Learning Missing-Modality Learning
College of Computer Science, VCIP, DISSec, Nankai University, Tianjin, China · School of Software, Tiangong University, Tianjin, China · Shenzhen Campus of Sun Yat-sen University, Shenzhen, China +1

Jun 4, 2026, cs.LGJ/K move · Enter open · S save

Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion

Dongxiao He, Ruqiong Zhang, Zhizhi Yu, Ling Ding+3

College of Intelligence and Computing, Tianjin University

Graph Neural Networks KG Completion

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Knowledge Graph Completion (KGC) aims at predicting missing triplets from incomplete knowledge graphs, which is crucial for downstream applications. Recently, Graph Neural Network (GNN)-based methods have achieved remarkable success by performing message passing over query-centered local subgraphs. However, in practice, a query is jointly defined by both the entity and the relation, with both carrying information indispensable for reasoning, yet these methods rely solely on the query relation as the guiding signal, while the information inherent in the query entity is not leveraged to guide inference - the entity serves merely as a structural anchor for subgraph extraction. To this end, we incorporate query entity information into the reasoning process from two perspectives: the first is structural context, i.e., the neighboring structure and relation patterns around the entity, which is encoded by a dedicated context encoder and used to modulate messages; the second is semantic type of the entity, inferred by a large language model, which is incorporated into attention computation and final scoring to provide type-level prior constraints. Together, these two sources of information enable the reasoning process to be guided by both the query relation and the query entity. Experimental results on standard benchmarks demonstrate the effectiveness of the proposed Q-GNN.

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