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. In particular, the life sciences 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.