SOTAVerified

Knowledge Graph Completion

Knowledge graphs $G$ are represented as a collection of triples $\{(h, r, t)\}\subseteq E\times R\times E$, where $E$ and $R$ are the entity set and relation set. The task of Knowledge Graph Completion is to either predict unseen relations $r$ between two existing entities: $(h, ?, t)$ or predict the tail entity $t$ given the head entity and the query relation: $(h, r, ?)$.

Source: One-Shot Relational Learning for Knowledge Graphs

Papers

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Showing 251–300 of 482 papers

TitleStatusHype
KG-NSF: Knowledge Graph Completion with a Negative-Sample-Free Approach—0
KGRefiner: Knowledge Graph Refinement for Improving Accuracy of Translational Link Prediction Methods—0
KG-TRICK: Unifying Textual and Relational Information Completion of Knowledge for Multilingual Knowledge Graphs—0
KGxBoard: Explainable and Interactive Leaderboard for Evaluation of Knowledge Graph Completion Models—0
Knowledge Graph and Text Jointly Embedding—0
Knowledge Graph Assisted Automatic Sports News Writing—0
Knowledge Graph Completion as Tensor Decomposition: A Genreal Form and Tensor N-rank Regularization—0
Knowledge Graph Completion Method Combined With Adaptive Enhanced Semantic Information—0
Knowledge Graph Completion Models are Few-shot Learners: An Empirical Study of Relation Labeling in E-commerce with LLMs—0
Knowledge Graph Completion to Predict Polypharmacy Side Effects—0
Knowledge Graph Completion with Counterfactual Augmentation—0
Knowledge Graph Completion with Pre-trained Multimodal Transformer and Twins Negative Sampling—0
Knowledge Graph Completion with Text-aided Regularization—0
Knowledge Graph Embedding: A Probabilistic Perspective and Generalization Bounds—0
Knowledge Graph Embedding via Graph Attenuated Attention Networks—0
Knowledge Graphs: Opportunities and Challenges—0
Knowledge Reasoning via Jointly Modeling Knowledge Graphs and Soft Rules—0
Knowledge Transfer with Medical Language Embeddings—0
Learning Structured Embeddings of Knowledge Graphs with Adversarial Learning Framework—0
Learning to Borrow– Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion—0
Incomplete Knowledge Graph Alignment—0
LLM-Assisted Knowledge Graph Completion for Curriculum and Domain Modelling in Personalized Higher Education Recommendations—0
Logical Reasoning with Relation Network for Inductive Knowledge Graph Completion—0
Membership Inference Attacks on Knowledge Graphs—0
Mining On Alzheimer's Diseases Related Knowledge Graph to Identity Potential AD-related Semantic Triples for Drug Repurposing—0
Mixed-Curvature Multi-Relational Graph Neural Network for Knowledge Graph Completion—0
Mixture-of-Graphs: Zero-shot Relational Learning for Knowledge Graph by Fusing Ontology and Textual Experts—0
MoCoKGC: Momentum Contrast Entity Encoding for Knowledge Graph Completion—0
MoCoSA: Momentum Contrast for Knowledge Graph Completion with Structure-Augmented Pre-trained Language Models—0
MuCo-KGC: Multi-Context-Aware Knowledge Graph Completion—0
MulDE: Multi-teacher Knowledge Distillation for Low-dimensional Knowledge Graph Embeddings—0
Multi-level Shared Knowledge Guided Learning for Knowledge Graph Completion—0
Multilingual Knowledge Graph Completion with Joint Relation and Entity Alignment—0
Multi-view Classification Model for Knowledge Graph Completion—0
Neural, Symbolic and Neural-Symbolic Reasoning on Knowledge Graphs—0
Neural Tensor Networks with Diagonal Slice Matrices—0
NeuSTIP: A Novel Neuro-Symbolic Model for Link and Time Prediction in Temporal Knowledge Graphs—0
NoiGAN: NOISE AWARE KNOWLEDGE GRAPH EMBEDDING WITH GAN—0
One Subgraph for All: Efficient Reasoning on Opening Subgraphs for Inductive Knowledge Graph Completion—0
On Event-Driven Knowledge Graph Completion in Digital Factories—0
On Large-scale Evaluation of Embedding Models for Knowledge Graph Completion—0
On Multi-Relational Link Prediction with Bilinear Models—0
On the Aggregation of Rules for Knowledge Graph Completion—0
On the Equivalence of Holographic and Complex Embeddings for Link Prediction—0
Toward Understanding The Effect Of Loss function On Then Performance Of Knowledge Graph Embedding—0
On the Use of Entity Embeddings from Pre-Trained Language Models for Knowledge Graph Completion—0
Ontology-enhanced Prompt-tuning for Few-shot Learning—0
Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion—0
P-INT: A Path-based Interaction Model for Few-shot Knowledge Graph Completion—0
Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion—0
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All datasetsFB15k-237DBP-5L (English)DBP-5L (Greek)DPB-5L (French)WN18RRDBbook2014MovieLens 1M

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1KBGATHits@1062.6—Unverified
2HAKEHits@1054.2—Unverified
3PKGCHits@1048.7—Unverified
4KBATHits@146—Unverified
#ModelMetricClaimedVerifiedStatus
1JMACMRR44.6—Unverified
2AlignKGCMRR41.3—Unverified
3SS-AGAMRR32.1—Unverified
#ModelMetricClaimedVerifiedStatus
1JMACMRR71.7—Unverified
2AlignKGCMRR69.4—Unverified
3SS-AGAMRR35.3—Unverified
#ModelMetricClaimedVerifiedStatus
1JMACMRR64.5—Unverified
2AlignKGCMRR59.5—Unverified
3SS-AGAMRR36.6—Unverified
#ModelMetricClaimedVerifiedStatus
1HAKEHits@30.52—Unverified
2KBGATHits@30.48—Unverified
#ModelMetricClaimedVerifiedStatus
1KTUP (soft)Hits@1060.75—Unverified
#ModelMetricClaimedVerifiedStatus
1KTUP (soft)Hits@1048.9—Unverified