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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 26–45 of 45 papers

TitleStatusHype
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
Accurate Text-Enhanced Knowledge Graph Representation Learning—0
Probabilistic Reasoning via Deep Learning: Neural Association Models—0
Using Knowledge Graphs for Performance Prediction of Modular Optimization Algorithms—0
Debate Dynamics for Human-comprehensible Fact-checking on Knowledge Graphs—0
Pre-training Language Model Incorporating Domain-specific Heterogeneous Knowledge into A Unified Representation—0
Improving Knowledge Graph Representation Learning by Structure Contextual Pre-training—0
Triple Classification for Scholarly Knowledge Graph Completion—0
Revisiting Evaluation of Knowledge Base Completion Models—0
Self-supervised Quantized Representation for Seamlessly Integrating Knowledge Graphs with Large Language Models—0
Efficient Relational Context Perception for Knowledge Graph Completion—0
Knowledge Representation via Joint Learning of Sequential Text and Knowledge Graphs—0
Knowlege Graph Embedding by Flexible Translation—0
DOLORES: Deep Contextualized Knowledge Graph Embeddings—0
Structural damage detection via hierarchical damage information with volumetric assessment—0
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Benchmark Results

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