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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 76–100 of 156 papers

TitleStatusHype
KGBoost: A Classification-based Knowledge Base Completion Method with Negative Sampling—0
Knowledge Base Completion: Baselines Strike Back—0
A Survey on Graph Neural Networks for Knowledge Graph Completion—0
Combining Rules and Embeddings via Neuro-Symbolic AI for Knowledge Base Completion—0
Representing Text for Joint Embedding of Text and Knowledge Bases—0
Combining Representation Learning with Logic for Language Processing—0
Knowledge Base Completion using Web-Based Question Answering and Multimodal Fusion—0
Knowledge Base Completion via Coupled Path Ranking—0
Revisiting Evaluation of Knowledge Base Completion Models—0
Knowledge Graph Embedding via Graph Attenuated Attention Networks—0
Knowledge Graph Embedding with Hierarchical Relation Structure—0
Knowledge Graphs: The Future of Data Integration and Insightful Discovery—0
Why a Naive Way to Combine Symbolic and Latent Knowledge Base Completion Works Surprisingly Well—0
Revisiting the Task of Scoring Open IE Relations—0
CEAR: Cross-Entity Aware Reranker for Knowledge Base Completion—0
Causal Lifting and Link Prediction—0
Large Language Model-Enhanced Symbolic Reasoning for Knowledge Base Completion—0
Riemannian TransE: Multi-relational Graph Embedding in Non-Euclidean Space—0
Learning Conceptual Space Representations of Interrelated Concepts—0
Towards a Continuous Knowledge Learning Engine for Chatbots—0
Learning Multi-Relational Semantics Using Neural-Embedding Models—0
Learning Relation Representations from Word Representations—0
Link Prediction using Embedded Knowledge Graphs—0
Capacity and Bias of Learned Geometric Embeddings for Directed Graphs—0
Meta Reasoning over Knowledge Graphs—0
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