Ideas:

• Applications for learning physics. The graph view of the semantic network makes it possible to focus on a specific topic and view all relevant connections to other topics. This could make it easier to get a interlinked picture of the physics landscape when using semantic network tools while learning physics knowledge.

• Applications for scientific publication and data exchange:

  • Structuring data of experiments within a ontology to make it easily reusable

  • Structuring scientific publications as semantic network knowledge graphs

  • Connecting knowledge of different scientists into one knowledge graph

• Applications for knowledge extraction from papers. Extracting the most important keywords and relations from papers

• Applications for reasoning about physical knowledge to explore new theories

• Exploring the inner knowledge bases of large language models.

• Exploring the structure of the physics landscape

  • Identify different subject areas and find out how they are connected with each other

  • Exploring the structure of physics publications

The question arises for what purpose the techniques described above for representing derivations as a semantic network could be used. To answer this question we have to distinguish between two main use cases of such a network: One is the representation of a derivation for illustrative purposes. The other is the representation for automated prove checking.

In the first case the main purpose of the semantic network would be to give the viewer an overview of the logical steps that were used to perform the derivation.