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Knowledge Graphs have emerged as a paradigm for representing data and semantics, providing new possibilities for a wide variety of data-driven tasks (e.g., data integration, data analytics, search, query answering, entity disambiguation, etc.). Driven by a new generation of Web and enterprise applications, advances in NoSQL graph databases, as well as the need for enhanced learning, the technology behind Knowledge Graphs is now maturing to a level that has become practical in various domains. The aim of this thesis is to study the applicability of Knowledge Graphs in the area of (network) neuroscience for modeling and analyzing how the brain works. More specifically, the thesis will cover design of Knowledge Graphs for modelling physical pathways that build functional networks from which cognitive capacities emerge, generation and storage of data in such Knowledge Graphs, as well as analytics on the data. The aim is to test the suitability of the Knowledge Graphs paradigm in helping to understand how the brain works.
Related relevant background article can be found here.
Successful data science requires a mixture of statistics, computer science and domain knowledge. In DataScience@UiO, we bridge the first of these gaps by bringing statisticians and computer scientists from the Factulty of Mathematics and Natural Sciences together, and the other by bringing in industry-heavy research centers such as BigInsight and SIRIUS. In this blog-post, I want to say something about why I believe SIRIUS has a very important role to play in the current and future data science engagement at the University of Oslo.