Biological Knowledge Graphs: Examples and Construction Approaches

The slides in this repository were developed for Dr. Marco Mesiti's, University of Milan, course titled "Constructing Knowledge Graphs for Advanced Biomedical Applications" (https://homes.di.unimi.it/mesiti/phdcourse2023/ ). Taken from the course website, the course description reads:

Knowledge graphs (KGs) can be used as an integration means of heterogenous biomedical concepts and relationships existing in different biological data sources. The presence of various information related to the same topic (e.g. patients, therapies, diseases) can be exploited for tackling many biomedical problems, such as finding new treatments for existing drugs, aiding efforts to diagnose patients, and identifying associations between diseases and biomolecules. In this course, we will discuss the characteristics of knowledge graphs, the languages for their representation and querying, and the systems used for their storage. Then, we will discuss machine learning techniques that can be used to construct and analyze knowledge graphs in different biomedical applications. Finally, we will discuss techniques and examples for the construction of biological knowledge graphs.

Slides Overview: These slides cover Block 3 of the course, which is focused on the construction of knowledge graphs within the biomedical domain. The lectures provides an introduction to and overview of the Semantic Web and an overview of biomedical knowledge graphs and their applications within the biomedical domain. It also describes three different approaches for constructing knowledge graphs with example GitHub projects and concludes by describing the PheKnowLator Ecosystem, including the technical infrastructure and its biomedical applications.