Efficient Few-shot Link Prediction on Hyper-relational Knowledge Graphs
This paper introduces a new task called Few-Shot Link Prediction on Hyper-relational Facts (FSLPHFs), which aims to predict a missing entity in a hyper-relational fact with limited support instances. The authors propose MetaRH, a model that learns Meta Relational information in Hyper-relational facts to accurately predict the missing entity.
Integrating Multi-curvature Shared and Specific Embedding for Temporal Knowledge Graph Completion
The proposed IME model effectively captures the complex geometric structures of temporal knowledge graphs by simultaneously modeling them in multi-curvature spaces, including hyperspherical, hyperbolic, and Euclidean spaces. IME learns both space-shared and space-specific properties to mitigate spatial gaps and comprehensively capture characteristic features across different curvature spaces.
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