Common basic knowledge for artificial intelligence algorithms
Completed on 05-Jul-2015 (19 days)
The proposed system is basically a big layered classification of AI (training) data, whose definition was started in the previous section
by explaining the main category (i.e., Layers, associated with individual elements, as opposed to concepts). In the current section, I will be completing this definition with a detailed description of the main constituent elements of the system.
All the information accounted by this system is expected to be defined on account of the following basic elements:
By putting together all the ideas explained in this section and in the previous one, the (ranked) layered categorisation which underlies the proposed system is formed by the following parts:
- Two groups of elements which are ordered according to two different rankings and are externally relevant: Entities (Ranking E) & Concepts (Ranking C, which is redefined as many times as Entities with associated Concepts).
- Two additional groups of elements which are accessorily used by the two ones in the previous point: Properties (Ranking P) & Connectors (unranked).
- On top of all these rankings, there is another one (Ranking L) formed by enum-like elements called Layers, which defines the two aforementioned externally-relevant elements/rankings: Entities & Ranking E in a direct way; and Concepts & Ranking C indirectly (on account of the fact that Concepts are always built on Entities).
Note that the words used to define a Layer or its (enum-)elements may also be treated as Entities or Properties. In fact, Layers are not elements of this system in a strict sense (i.e., at the same level than all the ones defined in this section); they are just a way to facilitate the understanding and usage of the proposed Data format.