Dr. Michel Dumontier from Stanford University presents a lecture on "Ontologies."
Lecture Description
Ontology has its roots as a field of philosophical study that is focused on the nature of existence. However, today's ontology (aka knowledge graph) can incorporate computable descriptions that can bring insight in a wide set of compelling applications including more precise knowledge capture, semantic data integration, sophisticated query answering, and powerful association mining - thereby delivering key value for health care and the life sciences. In this webinar, I will introduce the idea of computable ontologies and describe how they can be used with automated reasoners to perform classification, to reveal inconsistencies, and to precisely answer questions. Participants will learn about the tools of the trade to design, find, and reuse ontologies. Finally, I will discuss applications of ontologies in the fields of diagnosis and drug discovery.
View slides from this lecture:
https://drive.google.com/open?id=0B4IAKVDZz_JUVjZuRVpMVDMwR0E
About the Speaker
Dr. Michel Dumontier is an Associate Professor of Medicine (Biomedical Informatics) at Stanford University. His research focuses on the development of methods to integrate, mine, and make sense of large, complex, and heterogeneous biological and biomedical data. His current research interests include (1) using genetic, proteomic, and phenotypic data to find new uses for existing drugs, (2) elucidating the mechanism of single and multi-drug side effects, and (3) finding and optimizing combination drug therapies. Dr. Dumontier is the Stanford University Advisory Committee Representative for the World Wide Web Consortium, the co-Chair for the W3C Semantic Web for Health Care and the Life Sciences Interest Group, scientific advisor for the EBI-EMBL Chemistry Services Division, and the Scientific Director for Bio2RDF, an open source project to create Linked Data for the Life Sciences. He is also the founder and Editor-in-Chief for a Data Science, a new IOS Press journal featuring open access, open review, and semantic publishing.
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