Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods
cs.LG
Submitted: 2025-06-05
Updated: 2026-09-06
Comments: 20 pages. Accepted for publication in IEEE Transactions on Knowledge and Data Engineering (TKDE). Author's accepted manuscript
Code: https://github.com/lijp12/SIR
Project page: https://msnews.github.io
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Neurosymbolic Methods for Dynamic Knowledge Graphs
- Commonsense for Generative Multi-Hop Question Answering Tasks
- Deep Hyperedges: a Framework for Transductive and Inductive Learning on Hypergraphs
- Knowledge Hypergraphs: Prediction Beyond Binary Relations
- A Survey on Temporal Knowledge Graph: Representation Learning and Applications
- On the representation and embedding of knowledge bases beyond binary relations
- Hyperbolic Hypergraph Neural Networks for Multi-Relational Knowledge Hypergraph Representation
- Message Passing for Hyper-Relational Knowledge Graphs
- Temporal Fact Reasoning over Hyper-Relational Knowledge Graphs
- Link Prediction on N-ary Relational Facts: A Graph-based Approach
- Efficient Estimation of Word Representations in Vector Space
- Hyper-SAGNN: a self-attention based graph neural network for hypergraphs
- HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs
- UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks
- HNHN: Hypergraph Networks with Hyperedge Neurons
- Be More with Less: Hypergraph Attention Networks for Inductive Text Classification
- TransA: An Adaptive Approach for Knowledge Graph Embedding
- STransE: a novel embedding model of entities and relationships in knowledge bases
- RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
- QuatDE: Dynamic Quaternion Embedding for Knowledge Graph Completion
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