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Holographic Embeddings of Knowledge Graphs

arXiv 1510.04935 ↗First posted 16 Oct 2015cs.AI cs.LG stat.MLcited by 181

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Abstract (arXiv)

Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs. In this work, we propose holographic embeddings (HolE) to learn compositional vector space representations of entire knowledge graphs. The proposed method is related to holographic models of associative memory in that it employs circular correlation to create compositional representations. By using correlation as the compositional operator HolE can capture rich interactions but simultaneously remains efficient to compute, easy to train, and scalable to very large datasets. In extensive experiments we show that holographic embeddings are able to outperform state-of-the-art methods for link prediction in knowledge graphs and relational learning benchmark datasets.

Key facts. Cited by 181 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Oct 2015.

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Citing papers per year: 2021: 54; 2022: 35; 2023: 38; 2024: 28; 2025: 17; 2026: 9.

181 papers.

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  1. 2023Large-scale Multi-Modal Pre-trained Models: A Comprehensive Surveycited by 153
  2. 2021Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Surveycited by 130
  3. 2022A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodalcited by 106
  4. 2021A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and Data Transformationscited by 99
  5. 2023Knowledge Graphs: Opportunities and Challengescited by 70
  6. 2022Knowledge Graph Embedding: A Survey from the Perspective of Representation Spacescited by 41
  7. 2022T2G-Former: Organizing Tabular Features into Relation Graphs Promotes Heterogeneous Feature Interactioncited by 41
  8. 2021Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Conceptscited by 37
  9. 2021Neuro-Symbolic Inductive Logic Programming with Logical Neural Networkscited by 36
  10. 2024Knowledge Graph Large Language Model (KG-LLM) for Link Predictioncited by 36
  11. 2021Vector Symbolic Architectures as a Computing Framework for Emerging Hardwarecited by 35
  12. 2021ChronoR: Rotation Based Temporal Knowledge Graph Embeddingcited by 34
38 more
  1. 2021Prototypical Representation Learning for Relation Extractioncited by 33
  2. 2022OneRel:Joint Entity and Relation Extraction with One Module in One Stepcited by 27
  3. 2021Learning with Holographic Reduced Representationscited by 25
  4. 2023A Multi-Task Perspective for Link Prediction with New Relation Types and Nodescited by 24
  5. 2025Large Language Models are Good Relational Learnerscited by 24
  6. 2023Editing Language Model-based Knowledge Graph Embeddingscited by 23
  7. 2023Double Equivariance for Inductive Link Prediction for Both New Nodes and New Relation Typescited by 21
  8. 2021DisenKGAT: Knowledge Graph Embedding with Disentangled Graph Attention Networkcited by 19
  9. 2022Geometry Interaction Knowledge Graph Embeddingscited by 18
  10. 2021A Survey of Knowledge Graph Embedding and Their Applicationscited by 17
  11. 2021AutoGEL: An Automated Graph Neural Network with Explicit Link Informationcited by 17
  12. 2024Research on Adverse Drug Reaction Prediction Model Combining Knowledge Graph Embedding and Deep Learningcited by 17
  13. 2021Link Prediction on N-ary Relational Data Based on Relatedness Evaluationcited by 15
  14. 2021Rot-Pro: Modeling Transitivity by Projection in Knowledge Graph Embeddingcited by 15
  15. 2021KGAP: Knowledge Graph Augmented Political Perspective Detection in News Mediacited by 14
  16. 2021Graph Collaborative Reasoningcited by 14
  17. 2023Recasting Self-Attention with Holographic Reduced Representationscited by 14
  18. 2022ExpressivE: A Spatio-Functional Embedding For Knowledge Graph Completioncited by 13
  19. 2022MMKGR: Multi-hop Multi-modal Knowledge Graph Reasoningcited by 13
  20. 2021Context-aware Entity Typing in Knowledge Graphscited by 12
  21. 2022ReFactor GNNs: Revisiting Factorisation-based Models from a Message-Passing Perspectivecited by 12
  22. 2021Membership Inference Attacks on Knowledge Graphscited by 11
  23. 2021Robust Knowledge Graph Completion with Stacked Convolutions and a Student Re-Ranking Networkcited by 11
  24. 2021Bilinear Scoring Function Search for Knowledge Graph Learningcited by 11
  25. 2021Knowledge Sheaves: A Sheaf-Theoretic Framework for Knowledge Graph Embeddingcited by 11
  26. 2021QuatDE: Dynamic Quaternion Embedding for Knowledge Graph Completioncited by 10
  27. 2021CORE: A Knowledge Graph Entity Type Prediction Method via Complex Space Regression and Embeddingcited by 10
  28. 2022KRACL: Contrastive Learning with Graph Context Modeling for Sparse Knowledge Graph Completioncited by 10
  29. 2022TripleRE: Knowledge Graph Embeddings via Tripled Relation Vectorscited by 10
  30. 2023Sem@K: Is my knowledge graph embedding model semantic-aware?cited by 10
  31. 2023Knowledge Graph Embedding: An Overviewcited by 10
  32. 2021Subgraph-aware Few-Shot Inductive Link Prediction via Meta-Learningcited by 9
  33. 2021Are Missing Links Predictable? An Inferential Benchmark for Knowledge Graph Completioncited by 9
  34. 2021Explainable Semantic Space by Grounding Language to Vision with Cross-Modal Contrastive Learningcited by 9
  35. 2023Temporal Inductive Path Neural Network for Temporal Knowledge Graph Reasoningcited by 9
  36. 2024Negative Sampling in Knowledge Graph Representation Learning: A Reviewcited by 9
  37. 2021Learning Visual Models using a Knowledge Graph as a Trainercited by 8
  38. 2021Efficient Relation-aware Scoring Function Search for Knowledge Graph Embeddingcited by 8

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