Inductive knowledge graph completion
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Benchmarks
Most implemented
Inductive Relation Prediction by Subgraph Reasoning
Inductive Entity Representations from Text via Link Prediction
S$^2$DN: Learning to Denoise Unconvincing Knowledge for Inductive Knowledge Graph Completion
Context-aware Inductive Knowledge Graph Completion with Latent Type Constraints and Subgraph Reasoning
Towards Better Benchmark Datasets for Inductive Knowledge Graph Completion
Inductive Knowledge Graph Completion with GNNs and Rules: An Analysis
Papers
Cumulative Path-Level Semantic Reasoning for Inductive Knowledge Graph Completion
Conventional Knowledge Graph Completion (KGC) methods aim to infer missing information in incomplete Knowledge Graphs (KGs) by leveraging existing information, which struggle to perform effectively in scenarios involving…
Inductive knowledge graph completionKnowledge GraphsS$^2$DN: Learning to Denoise Unconvincing Knowledge for Inductive Knowledge Graph Completion
Inductive Knowledge Graph Completion (KGC) aims to infer missing facts between newly emerged entities within knowledge graphs (KGs), posing a significant challenge. While recent studies have shown promising results in in…
DenoisingInductive knowledge graph completionKnowledge Graph CompletionKnowledge GraphsContext-aware Inductive Knowledge Graph Completion with Latent Type Constraints and Subgraph Reasoning
Inductive knowledge graph completion (KGC) aims to predict missing triples with unseen entities. Recent works focus on modeling reasoning paths between the head and tail entity as direct supporting evidence. However, the…
Inductive knowledge graph completionKnowledge Graph CompletionTowards Better Benchmark Datasets for Inductive Knowledge Graph Completion
Knowledge Graph Completion (KGC) attempts to predict missing facts in a Knowledge Graph (KG). Recently, there's been an increased focus on designing KGC methods that can excel in the {\it inductive setting}, where a port…
Inductive knowledge graph completionKnowledge Graph CompletionQuery-Enhanced Adaptive Semantic Path Reasoning for Inductive Knowledge Graph Completion
Conventional Knowledge graph completion (KGC) methods aim to infer missing information in incomplete Knowledge Graphs (KGs) by leveraging existing information, which struggle to perform effectively in scenarios involving…
Inductive knowledge graph completionKnowledge Graph CompletionKnowledge GraphsLogical Reasoning with Relation Network for Inductive Knowledge Graph Completion
Inductive knowledge graph completion (KGC) aims to infer the missing relation for a set of newly-coming entities that never appeared in the training set. Such a setting is more in line with reality, as real-world KGs are…
Inductive knowledge graph completionKnowledge Graph CompletionLogical ReasoningRelation+1