One of the key components of Minerva’s AI technology is the powerful logical structure it applies to the knowledge with which it reasons. This is possible due to the ontological control employed by Minerva during knowledge capture. To enable meaning results, a consistent vocabulary must be used for both the data and the predictions. This requires a domain specific ontology so that terms are used consistently.  

Minerva uses taxonomies defined strictly according to the Aristotelian principles described in two books on AI co-authored by Minerva co-founder and Chief Software Architect Professor David Poole (please see the chapter on Reasoning with Uncertainty). Without reference to such taxonomies, it is very difficult to engineer AI applications which emulate intelligent reasoning as it would be carried out by a human expert.

For example, geologists need to use scientific vocabularies to describe their exploration targets and the environments they occur in. The words in these vocabularies occur within sometimes complex taxonomies, such as the taxonomy of rocks, the taxonomy of minerals and the taxonomy of geological time, to mention only a few. The Minerva AI Platform incorporates these taxonomies into their reasoning (they know, for example, that basalt is a volcanic rock, but granite is not). 

For this reason, Minerva is a strong supporter of and contributor to the development and maintenance of internationally-curated vocabulary standards, such as the INSPIRE initiative in the European Union. Minerva is working with a number of the INSPIRE committees responsible for creating the standards for the data and is contributing to its improvement by identifying problems in the standards when the data is of insufficient quality for use with Minerva’s technology. 

To learn more about earth science taxonomies, please visit Commission for the Management and Application of Geoscience Information.

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