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 401–450 of 482 papers

TitleStatusHype
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
Predicting from a Different Perspective: A Re-ranking Model for Inductive Knowledge Graph Completion—0
Pre-training Transformers for Knowledge Graph Completion—0
Pretrain-KGEs: Learning Knowledge Representation from Pretrained Models for Knowledge Graph Embeddings—0
ProjB: An Improved Bilinear Biased ProjE model for Knowledge Graph Completion—0
Prompted Meta-Learning for Few-shot Knowledge Graph Completion—0
Pseudo-Riemannian Embedding Models for Multi-Relational Graph Representations—0
PubGraph: A Large-Scale Scientific Knowledge Graph—0
Quantum and Translation Embedding for Knowledge Graph Completion—0
QuatE-D: A Distance-Based Quaternion Model for Knowledge Graph Embedding—0
Query-Enhanced Adaptive Semantic Path Reasoning for Inductive Knowledge Graph Completion—0
RatE: Relation-Adaptive Translating Embedding for Knowledge Graph Completion—0
ReadE: Learning Relation-Dependent Entity Representation for Knowledge Graph Completion—0
Reinforced Anytime Bottom Up Rule Learning for Knowledge Graph Completion—0
Relation Adversarial Network for Low Resource Knowledge Graph Completion—0
Relation-Aware Network with Attention-Based Loss for Few-Shot Knowledge Graph Completion—0
Relation-dependent Contrastive Learning with Cluster Sampling for Inductive Relation Prediction—0
Relation Prediction for Unseen-Entities Using Entity-Word Graphs—0
Relations Prediction for Knowledge Graph Completion using Large Language Models—0
Re-thinking Knowledge Graph Completion Evaluation from an Information Retrieval Perspective—0
Rethinking Regularization Methods for Knowledge Graph Completion—0
Retrieval, Reasoning, Re-ranking: A Context-Enriched Framework for Knowledge Graph Completion—0
Revisiting Evaluation of Knowledge Base Completion Models—0
RotateQVS: Representing Temporal Information as Rotations in Quaternion Vector Space for Temporal Knowledge Graph Completion—0
RTFE: A Recursive Temporal Fact Embedding Framework for Temporal Knowledge Graph Completion—0
Scientific Hypothesis Generation and Validation: Methods, Datasets, and Future Directions—0
Separate-and-Aggregate: A Transformer-based Patch Refinement Model for Knowledge Graph Completion—0
Sequence-to-Sequence Knowledge Graph Completion and Question Answering—0
Simple Rule Injection for ComplEx Embeddings—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