This article discusses GNN-RAG, a new AI method that combines the language understanding abilities of LLMs with the reasoning abilities of GNNs for Retrieval-Augmented Generation (RAG) style. This approach improves KGQA performance by utilizing GNNs for retrieval and RAG for reasoning.
How can an LLM be applied effectively for biomedical entity linking? Entity linking involves recognizing and extracting entities within the text and mapping them to standardized concepts in a large terminology. I
Leverage LLM-enhanced natural language processing and traditional machine learning techniques are used to extract structure and to build a knowledge graph from unstructured corpus.
an experiment with Google Bard AI, utilizing a Knowledge Graph for semantic compression to improve language model integration and narrative continuity in fiction.
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