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TuckER: Tensor Factorization for Knowledge Graph Completion

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arxiv 1901.09590 v2 pith:H6HIPFYG submitted 2019-01-28 cs.LG stat.ML

Ivana Balažević , Carl Allen , Timothy M. Hospedales This is my paper · ORCID
classification cs.LGstat.ML
keywords tuckerfactsknowledgemodelsgraphlinearlinkmodel
verification ladder T0 review T1 audit T2 compute T3 formal

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Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is a task of inferring missing facts based on existing ones. We propose TuckER, a relatively straightforward but powerful linear model based on Tucker decomposition of the binary tensor representation of knowledge graph triples. TuckER outperforms previous state-of-the-art models across standard link prediction datasets, acting as a strong baseline for more elaborate models. We show that TuckER is a fully expressive model, derive sufficient bounds on its embedding dimensionalities and demonstrate that several previously introduced linear models can be viewed as special cases of TuckER.

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Forward citations

Cited by 14 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. MEDMKG: Benchmarking Medical Knowledge Exploitation with Multimodal Knowledge Graph

    cs.AI 2025-05 conditional novelty 6.0 of 10

    MEDMKG is a multimodal medical knowledge graph linking chest X-ray images to UMLS concepts, with a Neighbor-aware Filtering algorithm and benchmarks showing mixed but often positive downstream gains.

  4. AppGen: Mobility-aware App Usage Behavior Generation for Mobile Users

    cs.HC 2024-12 conditional novelty 6.0 of 10

    An autoregressive diffusion model generates realistic mobile app usage sequences from users' spatio-temporal trajectories, beating state-of-the-art baselines on distributional fidelity metrics on two telecom datasets.

  5. Deep Sparse Latent Feature Models for Knowledge Graph Completion

    cs.CL 2024-11 conditional novelty 6.0 of 10

    DSLFM-KGC couples sparse latent community models with BERT text features and contrastive learning, improving state-of-the-art scores on Wikidata5M-Ind, WN18RR, and UMLS.

  6. AgREE: Agentic Reasoning for Knowledge Graph Completion on Emerging Entities

    cs.AI 2025-08 conditional novelty 5.0 of 10

    A zero-training LLM agent that iteratively retrieves and reflects over search results can complete knowledge graph triples about emerging entities better than trained KGC models, the authors report.

  7. A Semantic Partitioning Method for Large-Scale Training of Knowledge Graph Embeddings

    cs.LG 2025-01 conditional novelty 5.0 of 10

    A class-based partitioning heuristic for parallel knowledge graph embedding training, evaluated on FB15K and FB15K-237, shows modest and model-dependent gains over random partitioning.

  8. Efficiently Generating Multidimensional Calorimeter Data with Tensor Decomposition Parameterization

    cs.LG 2025-08 conditional novelty 4.0 of 10

    Generating CPD tensor factors instead of full tensors inside GANs and diffusion models can cut output parameters by roughly 80-90% while keeping similar FID scores on calorimeter data.

  9. 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.

  10. KG-BiLM: Knowledge Graph Embedding via Bidirectional Language Models

    cs.CL 2025-06 conditional novelty 4.0 of 10

    A bidirectional decoder with a graph-aware attention mask, knowledge-masked prediction, and contrastive sub-graph alignment achieves reported state-of-the-art link prediction on Wikidata5M and competitive results on WN18RR.

  11. Rethinking Regularization Methods for Knowledge Graph Completion

    cs.LG 2025-05 reject novelty 4.0 of 10

    A selective sparsity regularizer, SPR, nudges link prediction metrics up on standard benchmarks, but the evidence is single-run and the theoretical justification is mathematically flawed.

  12. 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.

  13. Is Architectural Complexity Overrated? Competitive and Interpretable Knowledge Graph Completion with RelatE

    cs.CL 2025-05 reject novelty 4.0 of 10

    RelatE, a real-valued phase-modulus embedding model, achieves the best reported MRR on YAGO3-10 (0.521) but falls far behind RotatE on WN18RR and relies on flawed formal proofs.

  14. Enhancing Knowledge Graph Completion with GNN Distillation and Probabilistic Interaction Modeling

    cs.AI 2025-05 conditional novelty 4.0 of 10

    Small MRR gains on WN18RR and FB15K-237 follow from adding iterative message filtering and top-k probabilistic bilinear scoring to KGC models, but the merged-variant synergy claim is not supported by the reported numbers.

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