Introductory course syllabus: AIA Ontology

Unit of competence: Knowledge or skill needed to perform a specific task.

To be able to understand and describe and apply formal system accounting for how agents impact environments through their activities

1. Transmission

2. Assesment

3. Comformation 

Course

1. Why: (Understand the importance human impact on the integrated environment and why we need a common language (understandable by humans and machines) to describe human impact ) [Problem]

   1. Why climate

      1. Our actions have consequences 

   2. Why AIA

      1. Premise 1: An agent enganges in an activity that impacts an environment

   3. Why ontologies (human and machines understand)

      1. Data, representation and processing, structure and meaning

      2. Semantic Web (https://cambridgesemantics.com/blog/semantic-university/learn-owl-rdfs/owl-101/#:~:text=OWL%20is%20now%20the%20ontology,Expressive%20and%20flexible%20data%20modeling)

         1. Content understandable and available in machine procesable form (programmable knowledge)

   4. Why anthropogenic impact analysis

2. How [Method]

   1. How the Semantic Web works 
      
      

      1. RDF (https://www.w3.org/RDF/)

         1. “RDF is a standard model for data interchange on the Web. RDF has features that facilitate data merging even if the underlying schemas differ, and it specifically supports the evolution of schemas over time without requiring all the data consumers to be changed.

         2. RDF extends the linking structure of the Web to use URIs to name the relationship between things as well as the two ends of the link (this is usually referred to as a “triple”). Using this simple model, it allows structured and semi-structured data to be mixed, exposed, and shared across different applications.

         3. This linking structure forms a directed, labelled graph, where the edges represent the named link between two resources, represented by the graph nodes. This graph view is the easiest possible mental model for RDF and is often used in easy-to-understand visual explanations.”

      2. OWL,

         1. Expressive and flexible data modelling

         2. Efficient automated reasoning

         3. “One of the distinguishing features of OWL is that it can be used to express extremely complicated and subtle ideas about your data. OWL specifies concepts, relationships, as well as characteristics of concepts and relationships in a human and machine-understandable model.

      3. Knowledge graphs

3. Intro to family of ontologies (aiao, claimont, impactont, infocomm) [what aspect of the problem does it solve]

   1. Axioms

   2. Definitions: Agents, Activities, and Environment

   3. OWL, TTL, etc

   4. Use Cases

4. INFOCOMM:

   1. Axioms …..

   2. Definitions: 

   3. OWL, TTL, etc

   4. Example use case (fairly simple, just to demonstrate concept)

5. CLAIMONT

   1. Axioms …..

   2. Definitions: 

   3. OWL, TTL, etc

   4. Example use case (fairly simple, just to demonstrate concept)

6. IMPACTONT

   1. Axioms …..

   2. Definitions: 

   3. OWL, TTL, etc

   4. Example use case (fairly simple, just to demonstrate concept)

7. AIAO

   1. Axiom

   2. Definitions: Agents, Activities, and Environment

   3. OWL, TTL, etc

   4. Use cases - combined with the 3 others. The AIAO uses them all

8. Next steps

   1. Mapping AIAO to existing standards  and vocabularies

   2. (Provide a possibility to engage. Including the point of engagement)

   3. (What else is needed)

Course Structure Table