The main challenges for enterprise Large Language Model (LLM)-based solutions for Knowledge Graph Embedding (KGE) include the high cost and resource intensity of creating tailored Pre-trained Language Model (PLM)-based KGE solutions, the mismatch between public benchmark datasets and enterprise use cases due to structural differences, the need for robust methods to combine automated novelty detection with human-curated interventions, and the requirement for a shift from classification to representation learning to accommodate novelty and encode Knowledge Graph (KG) features.

Authors

• Person: Not available Organization: Frontiers
  Combining large language models with enterprise knowledge graphs

Sources

• Combining large language models with enterprise knowledge graphs www.frontiersin.org Frontiers via serper

Referenced by nodes (3)

• Knowledge Graph concept
• knowledge graph embeddings concept
• pre-trained language models concept