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Found 5 papers, 2 papers with code
CoRTEx: Contrastive Learning for Representing Terms via Explanations with Applications on Constructing Biomedical Knowledge Graphs
Due to a lack of knowledge, previous contrastive learning models trained with Unified Medical Language System (UMLS) synonyms struggle at clustering difficult terms and do not generalize well beyond UMLS terms.
Large Language Models are Zero Shot Hypothesis Proposers
We subsequently evaluate the hypothesis generation capabilities of various top-tier instructed models in zero-shot, few-shot, and fine-tuning settings, including both closed and open-source LLMs.
Hierarchical Pretraining for Biomedical Term Embeddings
Electronic health records (EHR) contain narrative notes that provide extensive details on the medical condition and management of patients.
Automatic Biomedical Term Clustering by Learning Fine-grained Term Representations
Term clustering is important in biomedical knowledge graph construction.
BIOS: An Algorithmically Generated Biomedical Knowledge Graph
For decades, these knowledge graphs have been developed via expert curation; however, this method can no longer keep up with today's AI development, and a transition to algorithmically generated BioMedKGs is necessary.
