Structure pretraining and prompt tuning for knowledge graph transfer
Knowledge graphs (KG) are essential background knowledge providers in many tasks.
When designing models for KG-related tasks, one of the key tasks is to devise the …
When designing models for KG-related tasks, one of the key tasks is to devise the …
Transformer-based reasoning for learning evolutionary chain of events on temporal knowledge graph
Temporal Knowledge Graph (TKG) reasoning often involves completing missing factual
elements along the timeline. Although existing methods can learn good embeddings for …
elements along the timeline. Although existing methods can learn good embeddings for …
HyperFormer: Enhancing entity and relation interaction for hyper-relational knowledge graph completion
Asyncet: Asynchronous learning for knowledge graph entity ty** with auxiliary relations
Multi-modal knowledge graph transformer framework for multi-modal entity alignment
AsyncET: Asynchronous Representation Learning for Knowledge Graph Entity Ty**
Knowledge graph entity ty** (KGET) aims to predict the missing entity types in knowledge
graphs (KG). The relationship between entities and their corresponding types is often …
graphs (KG). The relationship between entities and their corresponding types is often …
TrustScore: Reference-Free Evaluation of LLM Response Trustworthiness
Large Language Models (LLMs) have demonstrated impressive capabilities across various
domains, prompting a surge in their practical applications. However, concerns have arisen …
domains, prompting a surge in their practical applications. However, concerns have arisen …