Which LLM is being used? What types of 'knowledge' are stored in the knowledge graph?

🤔 Which language model (LLM) are we using?
We're thrilled to announce that we're currently utilizing the powerful OpenAI models for this demo. For the creation process, we're specifically using the remarkable GPT-35 turbo model.

🌐 What knowledge is stored in the knowledge graph?
Our knowledge graph is an extensive treasure trove of information. We've meticulously curated it with a wide range of data sources, including WikiData, clinical trials, UniProt data, and much more. With over 40 million specific entities and 1,000 different predicates connecting them, our graph is a vast repository of knowledge. We even incorporate literature information, such as documents, authors, journals, and beyond.

💡 How can you implement this technology on your own graph?
We've made it incredibly accessible for you to leverage the power of natural language processing with your own graph. Simply identify the relevant elements you want to address in your graph and gather a set of high-quality examples. Then, provide this data to an OpenAI model and engage in a few rounds of shot-based learning. With a solid baseline of results, you can fine-tune and enhance your system to answer questions specific to your domain.

Don't miss out on this opportunity to dive into the world of advanced language models and knowledge graphs. Join us for our Q&A session and discover how you can unlock the potential of your own graph with OpenAI's groundbreaking technology. See you there! 👋😄

To learn more 👉 watch our Webinar "Beyond ChatGPT in Biomedicine: Advanced Q/A with Knowledge Graphs and GPT"

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