SOTAVerified

Triple Classification

Triple classification aims to judge whether a given triple (h, r, t) is correct or not with respect to the knowledge graph.

Papers

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Showing 26–45 of 45 papers

TitleStatusHype
Structural damage detection via hierarchical damage information with volumetric assessment—0
Pre-training Language Model Incorporating Domain-specific Heterogeneous Knowledge into A Unified Representation—0
Triple Classification for Scholarly Knowledge Graph Completion—0
Using Knowledge Graphs for Performance Prediction of Modular Optimization Algorithms—0
Knowledge Representation via Joint Learning of Sequential Text and Knowledge Graphs—0
Knowlege Graph Embedding by Flexible Translation—0
Iteratively Learning Representations for Unseen Entities with Inter-Rule CorrelationsCode0
KG-BERT: BERT for Knowledge Graph CompletionCode0
Knowledge Graph Refinement based on Triplet BERT-NetworksCode0
Image-embodied Knowledge Representation LearningCode0
Repurposing Knowledge Graph Embeddings for Triple Representation via Weak SupervisionCode0
TransINT: Embedding Implication Rules in Knowledge Graphs with Isomorphic Intersections of Linear SubspacesCode0
Embedding Ontologies via Incorporating Extensional and Intensional KnowledgeCode0
Learning Entity and Relation Embeddings for Knowledge Graph CompletionCode0
Does William Shakespeare REALLY Write Hamlet? Knowledge Representation Learning with ConfidenceCode0
Leveraging Pre-trained Language Models for Time Interval Prediction in Text-Enhanced Temporal Knowledge GraphsCode0
A Study on Knowledge Graph Embeddings and Graph Neural Networks for Web Of ThingsCode0
Progressive Knowledge Graph CompletionCode0
Differentiating Concepts and Instances for Knowledge Graph EmbeddingCode0
A Relational Memory-based Embedding Model for Triple Classification and Search PersonalizationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TransC (bern)Accuracy93.8—Unverified