A semantic network is a graph structure for representing knowledge in patterns of interconnected nodes and arcs. These structures can condense information about concepts or objects which might otherwise be scattered amongst many tables in a conventional relational database or buried in many pages of a scientific paper.
The success of AI applications often depends on the data used – Is there enough? Is it appropriate? Is it of sufficient quality? By using defined standards, semantic knowledge graphs help make data interpretable for humans and machines. Machines and algorithms make use of the semantic graphs to retrieve not only the objects themselves but also the relations that can be found between the objects, even if they are not explicitly stated. The Minerva AI Platform allows ‘reasoning’ based on the embedded expert knowledge contained within the semantic networks.
For example in Mineral Exploration, Minerva uses semantic networks to describe mineral deposits, deposit models or other exploration targets. Semantic networks are also used to describe locations on the ground, or underground, in terms of all available information.
For further details on semantic networks, you can read this article by John F. Sowa.