Data Exchange Podcast (Episode 242): Monthly Roundup with Paco Nathan

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  • Опубликовано: 7 ноя 2024

Комментарии • 3

  • @victorokrobodo9288
    @victorokrobodo9288 3 месяца назад +3

    I think Paco makes a very interesting and crucial point at the 30:35 mark. These LLM-powered graph builders are creating graphs from unstructured data, but how much domain knowledge do they possess to build truly sensible and accurate graphs in specific areas? For example, if I work with medical record systems and want to enhance them with data from medical guidelines, how confident can I be that the LLM understands the proper relationships between diseases, symptoms, and medical encodings like ICD-10 to generate a sensible and accurate graph?
    I know that some SciSpacy models have been trained on biomedical data and could theoretically do a better job of extracting relevant medical entities and relationships. How can this be incorporated into current GraphRAG workflows? I was hoping Paco would discuss this more and possibly explain ways to improve the resultant knowledge graph, either using existing approaches (Microsoft GraphRAG or Neo4J Graph Builder) or other alternative methods.

  • @positivevibes4521
    @positivevibes4521 2 месяца назад

    I love how you made your mind map in this video! Could you share the tools or techniques you used to create it? I’d love to make mine look as professional as yours.

  • @nityasarkar154
    @nityasarkar154 3 месяца назад

    💐💐💐💐💐💐💐💐💐💐💐🇮🇳🇮🇳🇮🇳🙏🙏🙏🙏🙏❤❤❤