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 hypothesis. 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 talk, 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.
Presented by:
Thomas Woodcock, MBA, PhD
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
Please click here to view a recording of this event and other past webinars.