Abstraction Awareness is about deeper understanding of abstraction, a concept so basic to human thinking. Subsequently abstraction is discussed by the means of basic graph theory and formal concept analysis.
Lets bring the definition of abstraction by subsumption, i.e. of not always lhs-unique mappings, to life. We want to detect complete subgraphs in graphs and build an abstract graph (model) from them. Think of this as building components, where their elements are extremely tightly bound. Notice that this is stronger than ‘connected components’ of graph theory.
Abstraction by ‘Siamese’ Edge
(rhs-unique) gives a pretty nice example of abstraction by subsumption, where rhs-uniqueness holds. We get two rhs nodes with an edge amongst them, that ‘survived’ the mapping.
(not-rhs-unique) – a kind of ‘siamese graph’ – leaves us with the choice to simply map the middle node to two different nodes, and then introduce an additional type of edge for ‘having common node’ (e.g. meaning ‘these two rooms have a common wall’) or to introduce any other kind of construct.
Abstraction by Concept Lattice
There is an elegant construct for bi-partite graphs (that correspond to an object – attribute, or extension – intension situation) from formal concept analysis:
(bi-partite) shows an example where nodes 1 and 2 together with a and b are ‘as complete as possible’ since they have all connections allowed amongst them in a bi-partite graph. This kind of completeness corresponds to a ‘concept’ in formal concept analysis. Thus we can draw a concept lattice as in (bi-partite), with 1-3 as objects and a-c as attributes. Notice that the edges here are read from bottom to top as generalisations, i.e. we can completely refrain from object-attribute edges here (no mesh up as by the ‘having common node’ edge above).