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Methods/RotatE

RotatE

GraphsIntroduced 200029 papers
Source Paper

Description

RotatE is a method for generating graph embeddings which is able to model and infer various relation patterns including: symmetry/antisymmetry, inversion, and composition. Specifically, the RotatE model defines each relation as a rotation from the source entity to the target entity in the complex vector space. The RotatE model is trained using a self-adversarial negative sampling technique.

Papers Using This Method

Is Architectural Complexity Overrated? Competitive and Interpretable Knowledge Graph Completion with RelatE2025-05-25SparseTransX: Efficient Training of Translation-Based Knowledge Graph Embeddings Using Sparse Matrix Operations2025-02-24Unified Interpretation of Smoothing Methods for Negative Sampling Loss Functions in Knowledge Graph Embedding2024-07-05PAC-Bayesian Generalization Bounds for Knowledge Graph Representation Learning2024-05-10Edge-Enabled Anomaly Detection and Information Completion for Social Network Knowledge Graphs2024-01-13Block-Diagonal Orthogonal Relation and Matrix Entity for Knowledge Graph Embedding2024-01-11Does Pre-trained Language Model Actually Infer Unseen Links in Knowledge Graph Completion?2023-11-15Model-based Subsampling for Knowledge Graph Completion2023-09-17BESS: Balanced Entity Sampling and Sharing for Large-Scale Knowledge Graph Completion2022-11-22CompoundE: Knowledge Graph Embedding with Translation, Rotation and Scaling Compound Operations2022-07-12Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation Learning2022-06-21Learning to Borrow -- Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion2022-04-27SpaceE: Knowledge Graph Embedding by Relational Linear Transformation in the Entity Space2022-04-21A Unified Framework for Rank-based Evaluation Metrics for Link Prediction in Knowledge Graphs2022-03-14SimKGC: Simple Contrastive Knowledge Graph Completion with Pre-trained Language Models2022-03-04Learning to Borrow– Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion2022-01-16CORE: A Knowledge Graph Entity Type Prediction Method via Complex Space Regression and Embedding2021-12-19Mixture-of-Graphs: Zero-shot Relational Learning for Knowledge Graph by Fusing Ontology and Textual Experts2021-11-16QubitE: Qubit Embedding for Knowledge Graph Completion2021-11-16Towards Robust Knowledge Graph Embedding via Multi-task Reinforcement Learning2021-11-11