The primary challenges of implementing corporate Knowledge Graph Embedding (KGE) solutions are categorized into four areas: (i) the quality and quantity of public or automatically annotated data, (ii) developing sustainable solutions regarding computational resources and longevity, (iii) adaptability of PLM-based KGE systems to evolving language and knowledge, and (iv) creating models capable of efficiently learning the Knowledge Graph (KG) structure.

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