Monday November 26 4:00 - 4:50 PM Kelley 1001
David Poole University of British Columbia Semantic Science: ontologies, data, and probabilistic theories This talk will overview work on "semantic science". The idea is that data is published using a rich ontology, and (probabilistic) theories make predictions on the data, and can be used for new cases. This is challenging when the data sets and the theories are heterogeneous, with overlapping coverage at various levels of abstraction and detail. This talk will concentrate on probabilistic theories that can make predictions on such data. These theories are typically about the existence and properties of objects. We discuss the problem of the probability of existence, and show how probabilities can use rich ontologies in written in modern ontology languages (such as OWL) using a form of definition advocated by Aristotle in 350 BC. Examples from earth sciences will be given. Finally we outline the remaining challenges before we get to the stage where: when someone (or some program) develops a new scientific theory, they can test it on all available data; when someone produces data, they can use it to evaluate all theories that make predictions on that data; and when someone has a new case they can use the best theories that make predictions on that data. Biography: David Poole is a Professor of Computer Science at the University of British Columbia. He received his Ph.D. from the Australian National University in 1984. He is known for his work on knowledge representation, default reasoning, assumption-based reasoning, diagnosis, reasoning under uncertainty, combining logic and probability, algorithms for probabilistic inference and representations for automated decision making. He is a co-author of an AI textbook, Computational Intelligence: A Logical Perspective (Oxford University Press, 1998), co-editor of the Proceedings of the Tenth Conference in Uncertainty in Artificial Intelligence (Morgan Kaufmann, 1994), is former associate editor and on the advisory board of the Journal of AI research, is an associate editor of AI Journal, is the secretary of the Association for Uncertainty in Artificial Intelligence, and is a Fellow of the American Association for Artificial Intelligence.
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