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SciBite / Knowledge Hub / Resources / Generating evidence-based hypotheses with AI-powered KG [Webinar]

Generating evidence-based hypotheses with AI-powered KG [Webinar]

Within the life sciences, the necessity for evidence-based decision-making is clear, where wrong decisions could result in dire consequences. In the rapidly evolving landscape of artificial intelligence (AI), knowledge graphs have emerged as pivotal resources for artificial intelligence tools and agents, offering a structured and scalable way to access a wealth of explicit and implicit knowledge and thus expedite the generation of hypotheses.
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By synthesizing information from diverse sources and integrating ontologies, these graphs facilitate a level of interoperability and scalability that is critical for the development of intelligent systems, particularly within the domain of life sciences. This presentation will introduce some of the approaches SciBite is taking to enhance and leverage knowledge graphs in the context of AI, including:
  • Create and enrich semantic networks by applying cutting edge AI-tools on top of our industry-leading NER tools
  • Support use case specific queries and analysis with fine-tuned vocabularies and edge embeddings
  • Using generative AI to let users talk to the knowledge graphs and get sensible human-readable answers

Through this webinar, we aim to showcase how cutting-edge techniques not only refine the structure and utility of knowledge graphs but also open new avenues for their application in AI-driven research and development.

Presenter(s): 

  • Thomas Woodcock, Technical Sales Manager, NA at SciBite
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Thomas Woodcock
Technical Sales Manager, North America, SciBite

Tom has over 20 years of experience in biological and pharmaceutical sciences, spanning multiple continents and roles in research, consulting, and education. He is responsible for all SciBite’s technical engagements across the US business, including partners, prospects, and existing customers. By focusing on data as a valuable asset, Tom leverages both his specialist scientific domain expertise and passion for data science to help others solve data challenges and inform decision making through

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