The Machine Learning Hub seminar series presents:
“Applying symbolic and sub-symbolic approaches to formalized knowledge bases in life sciences” by Dr. Robert Hoehndorf, Assistant Professor of Computer Science (CS) and Principal Investigator of Bio-Ontology Research Group (BORG). He is also the chair of the successful Artificial Intelligence for Genomics and Health (AI4GH) Seminar Series.
Wednesday, November 13
12:00 p.m. – 1:00 p.m.
Building 9, hall 2
Abstract:
Knowledge representation is a sub-field of AI which studies how to represent information about a domain so that is can be utilized for a wide range of tasks. The life sciences in particular have created a large amount of formalized knowledge bases. In my talk, I will show how to use information in formalized knowledge bases as background knowledge in statistical analyses and machine learning. I will discuss an algorithm to construct a map from formal theories into vector spaces that preserve the model-theoretic semantics of the
theories while enabling new operations within the vector space. Combining methods from knowledge representations with machine learning can be used to generate explanations and exploit background knowledge, which is particularly important in knowledge-intensive
disciplines such as biology and medicine.
To read the speaker’s bio and learn more about her work, please click here.
KAUST Machine Learning Hub
http://ml.kaust.edu.sa/