A Knowledge Graph-Based Semantic Database for Biomedical Sciences

About the talk

The proliferation of biological research data generated and shared openly online is of huge benefit to the scientific community, but there are often significant challenges to overcome before it can be integrated from different sources and re-used to gain new knowledge. This paper introduces BioGrakn, which is a graph-based deductive database, combining the power of knowledge graphs and machine reasoning. BioGrakn illustrates how data can be aggregated and integrated, modelled in all its complexity and contextual specificity, and extended as needed. Built upon GRAKN.AI, it provides an integrated, intelligent database for researchers handling complex data.

About the speaker

Christian graduated in Biochemistry with Management from Imperial College London. However, entrepreneurship won over lab work, and during his studies he started Student Upstarts, a seed investment company focused on getting recent graduates and students to start their own companies. Several have gone on to raise further funds, including Loot Bank who have gone on to raise £4mil. Having consulted for, invested in, and worked for countless startups over the last 8 years, he’s now joined GRAKN.AI as their Head of Sales, on a mission to get Life Science companies and beyond, to be able to connect their data and make new discoveries.