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
Differentially Private Federated Knowledge Graphs EmbeddingCode1
GreenKGC: A Lightweight Knowledge Graph Completion MethodCode1
Reasoning on Knowledge Graphs with Debate DynamicsCode1
On the Role of Conceptualization in Commonsense Knowledge Graph ConstructionCode1
CoDEx: A Comprehensive Knowledge Graph Completion BenchmarkCode1
Language Models as Knowledge EmbeddingsCode1
Structure Pretraining and Prompt Tuning for Knowledge Graph TransferCode1
Accurate Text-Enhanced Knowledge Graph Representation Learning—0
A Multimodal Translation-Based Approach for Knowledge Graph Representation Learning—0
Debate Dynamics for Human-comprehensible Fact-checking on Knowledge Graphs—0
DOLORES: Deep Contextualized Knowledge Graph Embeddings—0
Efficient Relational Context Perception for Knowledge Graph Completion—0
Improving Knowledge Graph Representation Learning by Structure Contextual Pre-training—0
Learning Structured Embeddings of Knowledge Graphs with Adversarial Learning Framework—0
Logic Rules Powered Knowledge Graph Embedding—0
Membership Inference Attacks on Knowledge Graphs—0
Neighborhood Mixture Model for Knowledge Base Completion—0
Neural Markov Logic Networks—0
OneRel:Joint Entity and Relation Extraction with One Module in One Step—0
On Large-scale Evaluation of Embedding Models for Knowledge Graph Completion—0
Probabilistic Reasoning via Deep Learning: Neural Association Models—0
Revisiting Evaluation of Knowledge Base Completion Models—0
Self-supervised Quantized Representation for Seamlessly Integrating Knowledge Graphs with Large Language Models—0
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Benchmark Results

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