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 1–25 of 45 papers

TitleStatusHype
Knowledge Representation Learning: A Quantitative ReviewCode2
Exploring Large Language Models for Knowledge Graph CompletionCode1
Structure Pretraining and Prompt Tuning for Knowledge Graph TransferCode1
GreenKGC: A Lightweight Knowledge Graph Completion MethodCode1
Language Models as Knowledge EmbeddingsCode1
Differentially Private Federated Knowledge Graphs EmbeddingCode1
CoDEx: A Comprehensive Knowledge Graph Completion BenchmarkCode1
On the Role of Conceptualization in Commonsense Knowledge Graph ConstructionCode1
Reasoning on Knowledge Graphs with Debate DynamicsCode1
On Large-scale Evaluation of Embedding Models for Knowledge Graph Completion—0
Self-supervised Quantized Representation for Seamlessly Integrating Knowledge Graphs with Large Language Models—0
Efficient Relational Context Perception for Knowledge Graph Completion—0
Structural damage detection via hierarchical damage information with volumetric assessment—0
Progressive Knowledge Graph CompletionCode0
Embedding Ontologies via Incorporating Extensional and Intensional KnowledgeCode0
A Study on Knowledge Graph Embeddings and Graph Neural Networks for Web Of ThingsCode0
Leveraging Pre-trained Language Models for Time Interval Prediction in Text-Enhanced Temporal Knowledge GraphsCode0
Iteratively Learning Representations for Unseen Entities with Inter-Rule CorrelationsCode0
Using Knowledge Graphs for Performance Prediction of Modular Optimization Algorithms—0
Knowledge Graph Refinement based on Triplet BERT-NetworksCode0
Repurposing Knowledge Graph Embeddings for Triple Representation via Weak SupervisionCode0
OneRel:Joint Entity and Relation Extraction with One Module in One Step—0
Improving Knowledge Graph Representation Learning by Structure Contextual Pre-training—0
Triple Classification for Scholarly Knowledge Graph Completion—0
Pre-training Language Model Incorporating Domain-specific Heterogeneous Knowledge into A Unified Representation—0
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Benchmark Results

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