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Image-embodied Knowledge Representation Learning

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arxiv 1609.07028 v2 pith:XLPVTOX6 submitted 2016-09-22 cs.CV cs.CL

Ruobing Xie , Zhiyuan Liu , Huanbo Luan , Maosong Sun This is my paper
classification cs.CVcs.CL
keywords knowledgerepresentationsimageslearningrepresentationentityinformationmodels
verification ladder T0 review T1 audit T2 compute T3 formal
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Entity images could provide significant visual information for knowledge representation learning. Most conventional methods learn knowledge representations merely from structured triples, ignoring rich visual information extracted from entity images. In this paper, we propose a novel Image-embodied Knowledge Representation Learning model (IKRL), where knowledge representations are learned with both triple facts and images. More specifically, we first construct representations for all images of an entity with a neural image encoder. These image representations are then integrated into an aggregated image-based representation via an attention-based method. We evaluate our IKRL models on knowledge graph completion and triple classification. Experimental results demonstrate that our models outperform all baselines on both tasks, which indicates the significance of visual information for knowledge representations and the capability of our models in learning knowledge representations with images.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods

    cs.LG 2025-06 conditional novelty 6.0 of 10

    A survey of n-ary knowledge representation learning methods proposes a two-dimensional taxonomy based on modeling technique and entity role/position awareness.

  2. HERGC: Heterogeneous Experts Representation and Generative Completion for Multimodal Knowledge Graphs

    cs.CL 2025-06 conditional novelty 6.0 of 10

    HERGC combines a multimodal expert-based retriever with a fine-tuned LLM re-ranker to achieve state-of-the-art multimodal knowledge graph completion on MKG-W, MKG-Y, and DB15K.

  3. Complementarity-driven Representation Learning for Multi-modal Knowledge Graph Completion

    cs.AI 2025-07 reject novelty 4.0 of 10

    MoCME combines expert-network fusion weighted by estimated mutual information and entropy-based negative sampling, and reports state-of-the-art multi-modal knowledge graph completion on five benchmarks.

  4. Towards Structure-aware Model for Multi-modal Knowledge Graph Completion

    cs.MM 2025-05 conditional novelty 4.0 of 10

    TSAM combines token-level fusion of visual and textual data with structure-anchored contrastive learning, outperforming prior multi-modal KGC models on DB15K, MKG-W, and MKG-Y.

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