An Ontologically Controlled Way to Compare The Metallurgical Characteristics of Mining Projects

Cognitive AI software using a metallurgy ontology can use semantic network descriptions of mineral deposits and mines to evaluate a mineral deposit and determine which deposit, anywhere in the world, most closely matches its metallurgical characteristics. After finding a suitable match, the user would be able to read the metallurgical report(s) of the similar deposit(s) to compare with their current understanding of the metallurgical considerations of their project. The final outcome would be to learn from the mistakes and triumphs of others, and view how mines with similar challenges handled their specific processing requirements.

Semantic network descriptions are entered into the system using a user interface that is part of MetMatch. The process involves reading a published paper or NI-43-101 about the deposit and capturing the relevant information in a structured way.

For the system to work as intended, a robust ontology of metallurgical concepts would need to be created, using an existing mineral deposit ontology as a guide.

Faculty Supervisor:

Alessandro Navarra


Alain Kabemba


Minerva Intelligence Inc.





McGill University



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