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 276–300 of 482 papers

TitleStatusHype
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