A5057174073- Artificial Intelligence in Healthcare and Education
- Topic Modeling
- Biomedical Text Mining and Ontologies
- Bioinformatics and Genomic Networks
- Natural Language Processing Techniques
- Semantic Web and Ontologies
University of California, San Francisco
2023-2024
Abstract Motivation Knowledge graphs (KGs) are being adopted in industry, commerce and academia. Biomedical KG presents a challenge due to the complexity, size heterogeneity of underlying information. Results In this work, we present Scalable Precision Medicine Open Engine (SPOKE), biomedical connecting millions concepts via semantically meaningful relationships. SPOKE contains 27 million nodes 21 different types 53 edges 55 downloaded from 41 databases. The graph is built on framework 11...
Abstract Motivation Large language models (LLMs) are being adopted at an unprecedented rate, yet still face challenges in knowledge-intensive domains such as biomedicine. Solutions pretraining and domain-specific fine-tuning add substantial computational overhead, requiring further domain-expertise. Here, we introduce a token-optimized robust Knowledge Graph-based Retrieval Augmented Generation (KG-RAG) framework by leveraging massive biomedical KG (SPOKE) with LLMs Llama-2-13b,...
Large Language Models (LLMs) have been driving progress in AI at an unprecedented rate, yet still face challenges knowledge-intensive domains like biomedicine. Solutions such as pre-training and domain-specific fine-tuning add substantial computational overhead, the latter require domain-expertise. External knowledge infusion is task-specific requires model training. Here, we introduce a task-agnostic Knowledge Graph-based Retrieval Augmented Generation (KG-RAG) framework by leveraging...