mlsanityData to 7 Oct 2026

Knowledge Graph Completion with Pre-trained Multimodal Transformer and Twins Negative Sampling

arXiv 2209.07084 ↗First posted 15 Sep 2022cs.AI cs.CLcited by 18

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Abstract (arXiv)

Knowledge graphs (KGs) that modelings the world knowledge as structural triples are inevitably incomplete. Such problems still exist for multimodal knowledge graphs (MMKGs). Thus, knowledge graph completion (KGC) is of great importance to predict the missing triples in the existing KGs. As for the existing KGC methods, embedding-based methods rely on manual design to leverage multimodal information while finetune-based approaches are not superior to embedding-based methods in link prediction. To address these problems, we propose a VisualBERT-enhanced Knowledge Graph Completion model (VBKGC for short). VBKGC could capture deeply fused multimodal information for entities and integrate them into the KGC model. Besides, we achieve the co-design of the KGC model and negative sampling by designing a new negative sampling strategy called twins negative sampling. Twins negative sampling is suitable for multimodal scenarios and could align different embeddings for entities. We conduct extensive experiments to show the outstanding performance of VBKGC on the link prediction task and make further exploration of VBKGC.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 25 of its 37 references that are in the map. First posted on arXiv 15 Sep 2022.

Builds on

25 of 37 references.

References · 25

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13 more
  1. 2019MMKG: Multi-Modal Knowledge Graphscited by 104
  2. 2017Knowledge Graph Completion via Complex Tensor Factorizationcited by 80
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  7. 2018NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embeddingcited by 42
  8. 2019Interaction Embeddings for Prediction and Explanation in Knowledge Graphscited by 40
  9. 2018Embedding Multimodal Relational Data for Knowledge Base Completioncited by 34
  10. 2020Structure Aware Negative Sampling in Knowledge Graphscited by 26
  11. 2021Efficient Non-Sampling Knowledge Graph Embeddingcited by 14
  12. 2021Billion-scale Pre-trained E-commerce Product Knowledge Graph Modelcited by 13
  13. 2022CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completioncited by 13

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 6; 2025: 5; 2026: 5.

18 papers.

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  1. 2022A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodalcited by 106
  2. 2024Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Surveycited by 50
  3. 2024NativE: Multi-modal Knowledge Graph Completion in the Wildcited by 23
  4. 2024Tokenization, Fusion, and Augmentation: Towards Fine-grained Multi-modal Entity Representationcited by 14
  5. 2024Multiple Heads are Better than One: Mixture of Modality Knowledge Experts for Entity Representation Learningcited by 13
  6. 2024Unleashing the Power of Imbalanced Modality Information for Multi-modal Knowledge Graph Completioncited by 10
  7. 2023Modality-Aware Negative Sampling for Multi-modal Knowledge Graph Embeddingcited by 7
  8. 2024Noise-powered Multi-modal Knowledge Graph Representation Frameworkcited by 7
  9. 2025Diffusion-based Hierarchical Negative Sampling for Multimodal Knowledge Graph Completioncited by 1
  10. 2025Transformer-Based Multimodal Knowledge Graph Completion with Link-Aware Contextscited by 1
  11. 2025DiffusionCom: Structure-Aware Multimodal Diffusion Model for Multimodal Knowledge Graph Completioncited by 1
  12. 2025Complementarity-driven Representation Learning for Multi-modal Knowledge Graph Completioncited by 1
6 more
  1. 2025Collaboration of Fusion and Independence: Hypercomplex-driven Robust Multi-Modal Knowledge Graph Completioncited by 0
  2. 2026Every Little Helps: Building Knowledge Graph Foundation Model with Fine-grained Transferable Multi-modal Tokenscited by 0
  3. 2026RADD: Retrieval-Augmented Discrete Diffusion for Multi-Modal Knowledge Graph Completioncited by 0
  4. 2026Time Imprint: Learning Time-Aware Representations in Multi-Modal Knowledge Graphscited by 0
  5. 2026MGDT: MLLM-Guided Diffusion Transformer with Relation-Adaptive Mixture-of-Experts for Multimodal Knowledge Graph Completioncited by 0
  6. 2026More Perspectives, Stronger Signals: Multi-Perspective Enhancement and Progressive Fusion for Multimodal Entity Representation Learningcited by 0