Problem
Research questions and friction points this paper is trying to address.
Knowledge Graph Completion
Pre-trained Language Models
Text Understanding and Graph Structure
Prompting Disentangled Embeddings for Knowledge Graph Completion with Pre-trained Language Model
🤖 AI Summary
To address the challenge of jointly modeling textual semantics and graph structural information in knowledge graph completion, this paper proposes a prompt-based disentangled representation framework. Methodologically, it is the first to integrate prompt tuning, disentangled variational autoencoding, and contrastive learning regularization: a pretrained language model (PLM) encodes contextual semantics of entities and relations; a disentangled encoder explicitly separates relational semantics from entity-role semantics; and contrastive learning regularizes the embedding space to preserve structural consistency. On standard benchmarks including FB15k-237, the approach achieves a 3.2% improvement in mean reciprocal rank (MRR) for link prediction, substantially outperforming TransE and GNN-based methods. Moreover, it enhances model generalization and interpretability by decoupling semantic and structural representations. This work establishes a novel paradigm for KG completion that balances deep semantic understanding with explicit structural clarity.
Innovation
Methods, ideas, or system contributions that make the work stand out.
PDKGC
Pre-trained Models
Prompt-based Learning
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