Decision making with rich ontologies for minerals exploration

Resource extraction, including mineral exploration and mining, is an important part of Canada’s economy. There is currently a great interest in finding the minerals that are needed for batteries; arguably the lack of minerals is the biggest impediment to cutting the price of electric vehicles and weaning society off fossil fuels. Artificial intelligence has great potential in all aspects of minerals exploration. The project has two aspects, the first is to learn symbolic descriptions of lineaments (e.g., faults) from sensor data, and the second is to use such symbolic descriptions (from sensor data and from human observations) to make better informed decisions. The is challenging because the observations use technical terms from multiple ontologies, and parts of the world are described at multiple levels of abstraction (in terms of subtypes) and detail (in terms of parts), and the descriptions often include missing data.

Faculty Supervisor:

David Poole


Amin Aghaee


Minerva Intelligence Inc.


Computer science


Natural resources




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