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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 11–20 of 156 papers

TitleStatusHype
BoxE: A Box Embedding Model for Knowledge Base CompletionCode1
Canonical Tensor Decomposition for Knowledge Base CompletionCode1
K-PLUG: KNOWLEDGE-INJECTED PRE-TRAINED LANGUAGE MODEL FOR NATURAL LANGUAGE UNDERSTANDING AND GENERATIONCode1
Compositional Learning of Relation Path Embedding for Knowledge Base Completion—0
A Factorization Machine Framework for Testing Bigram Embeddings in Knowledgebase Completion—0
Compositional Vector Space Models for Knowledge Base Completion—0
A Survey on Graph Neural Networks for Knowledge Graph Completion—0
Constructing large scale biomedical knowledge bases from scratch with rapid annotation of interpretable patterns—0
Commonsense Knowledge Base Completion and Generation—0
Combining Rules and Embeddings via Neuro-Symbolic AI for Knowledge Base Completion—0
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