SOTAVerified

Knowledge Base Completion

Knowledge base completion is the task which automatically infers missing facts by reasoning about the information already present in the knowledge base. A knowledge base is a collection of relational facts, often represented in the form of "subject", "relation", "object"-triples.

Papers

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Showing 51–75 of 156 papers

TitleStatusHype
Knowledge Graphs: The Future of Data Integration and Insightful Discovery—0
Extracting triples from dialogues for conversational social agents—0
CEAR: Cross-Entity Aware Reranker for Knowledge Base Completion—0
Modeling Large-Scale Structured Relationships with Shared Memory for Knowledge Base Completion—0
Feature-Rich Networks for Knowledge Base Completion—0
Generalized Graph Embedding Models—0
Context-aware Path Ranking for Knowledge Base Completion—0
HEAT: Hyperedge Attention Networks—0
Continuous and Interactive Factual Knowledge Learning in Verification Dialogues—0
HyperKG: Hyperbolic Knowledge Graph Embeddings for Knowledge Base Completion—0
Improved Knowledge Base Completion by Path-Augmented TransR Model—0
Improving Neural Knowledge Base Completion with Cross-Lingual Projections—0
Inferring Missing Entity Type Instances for Knowledge Base Completion: New Dataset and Methods—0
KGBoost: A Classification-based Knowledge Base Completion Method with Negative Sampling—0
Incorporating Selectional Preferences in Multi-hop Relation Extraction—0
Incorporating Relational Knowledge into Word Representations using Subspace Regularization—0
Causal Lifting and Link Prediction—0
Data-dependent Learning of Symmetric/Antisymmetric Relations for Knowledge Base Completion—0
Incorporating Both Distributional and Relational Semantics in Word Representations—0
Knowledge Base Completion: Baselines Strike Back—0
Incorporating Both Distributional and Relational Semantics in Word Representations—0
Augmenting Compositional Models for Knowledge Base Completion Using Gradient Representations—0
Distributional Negative Sampling for Knowledge Base Completion—0
Knowledge Graph Embedding via Graph Attenuated Attention Networks—0
Corpus-level Fine-grained Entity Typing Using Contextual Information—0
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