Analyzing Temporal Knowledge Graph Completion with Pre-trained Language Models
The author proposes a novel model, PPT, that leverages pre-trained language models and prompts to enhance temporal knowledge graph completion by accurately extracting temporal information and utilizing implied relations. The approach converts the task into a masked token prediction problem.
Pre-trained Language Model with Prompts for Temporal Knowledge Graph Completion
Proposing a novel TKGC model, PPT, that leverages pre-trained language models with prompts to effectively incorporate temporal information from knowledge graphs.
Temporal Knowledge Graph Completion with Time-sensitive Relations in Hypercomplex Space: A Novel Approach
Quaternion embeddings in hypercomplex space enhance temporal knowledge graph completion by capturing time-sensitive relations and periodic patterns.
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.
