The process for enriching the life sciences-oriented Sensigrafo knowledge graph involves the following steps: (1) marking entities in PubMed2 documents using the Cogito disambiguator, (2) generating possible relations using a distant supervision module grounded on Sensigrafo, (3) transforming documents into contextualized embeddings using a field-specific pre-trained language model like BioBERT, (4) performing adapter-based fine-tuning for relation extraction using contrastive learning, and (5) ranking predictions by model confidence.

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 (2)

• relation extraction concept
• contrastive learning concept