Knowledge Graph Embedding
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Benchmarks
FB15k
Most implemented
Inductive Relation Prediction by Subgraph Reasoning
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
MEIM: Multi-partition Embedding Interaction Beyond Block Term Format for Efficient and Expressive Link Prediction
Multi-Relational Embedding for Knowledge Graph Representation and Analysis
Papers
PyKEEN-NSX: A Modular Framework for Static, Dynamic and Schema-Aware Negative Sampling in PyKEEN
Embedding methods have become popular due to their scalability on link prediction and/or triple classification tasks on Knowledge Graphs (KGs). Embedding models are trained relying on both positive and negative samples o…
Knowledge Graph EmbeddingTriple ClassificationKnowledge GraphsLink PredictionNeural Regression with Embeddings for Numerical Attribute Prediction in Knowledge Graphs
In recent years, transductive knowledge graph embedding models have been applied to tasks such as link prediction and query answering. Although knowledge graphs often contain rich numerical attributes, most embedding mod…
Knowledge Graph EmbeddingKnowledge GraphsLink PredictionMatched Excess-Outranker Regularization for Candidate-Set Interference in Continual Knowledge Graph Embedding
Continual knowledge graph embedding updates entity and relation representations as a graph grows. Existing methods primarily address catastrophic forgetting, but entity admission also changes the candidate universe of ev…
Knowledge Graph EmbeddingTeRoR: Decoupled Temporal Rotation with Relational Circular Region for Temporal Knowledge Graph Embedding
In recent years, with the emergence of Temporal Knowledge Graphs (TKGs), research on learning entity and relation representations in TKGs has attracted increasing attention, giving rise to a large number of TKG embedding…
Knowledge Graph EmbeddingKnowledge GraphsHolographic Memory for Zero-Shot Compositional Reasoning in Knowledge Graphs: A Mechanistic Study of Where and Why It Fails
Knowledge graph embedding (KGE) models predict single-hop links well but have no mechanism for zero-shot compositional queries: multi-hop questions whose relation chains never appeared during training. Holographic Reduce…
Knowledge Graph EmbeddingKnowledge GraphsInferring Sensitive Attributes from Knowledge Graph Embeddings: Attack and Defense Strategies
Knowledge Graphs (KGs) are a powerful representation of linked data, offering flexibility, semantic richness, and support for knowledge enrichment and reasoning. They help data owners organize and exploit heterogeneous d…
Knowledge Graph EmbeddingKnowledge Graphs