Generic vs. Partitive Concept Systems

For the past couple of blogs I have been exploring different types on concept systems.  I have found these discussed, oddly enough, not in the literature on data modeling, but in the literature on terminology work.  At this point, I want to look at the two major concept systems.  These are very abundant in the raw material of information management, and require special attention.

Generic:  This is the familiar supertype-subtype concept system, where a more generic concept encompasses a range of more specific concepts.  E.g. Animal - Chordate - Vertebrate - Mammal - Primate - Homo sapiens.  There are a couple of interesting properties of this concept system:

• Any instance found in a specific concept is also covered by a more general concept.  The more general concepts possess fewer attributes than the more specific ones, but every specific concept possesses the attributes of each "parent" generic concept.  
•  Intention is inversely related to extension.   That is, the greater the number of specifying characteristics (intension), the more restricted the population of instances that is covered by the concept (extension).

Partitive: This is the part-whole concept system.  The study of part-whole relationships is called mereology.  It seems a bit odd to have a named discipline for this type of concept system, but not for others.  Perhaps it is an artifact of the evolution of philosophy.  Anyway, an example of a part whole system would be the organs of the human body, such as brain, liver, pancreas, kidney, and so on.  To have a complete view of the human body we would have to include tissues, such as epithelium, blood, muscle, nerves, etc.  This concept system is totally unlike the generic one as the parts have quite different identities that do not share characteristics.  We also run into interesting problems such as denial that the whole is anything more than the sum of its parts.  To summarize its properties:

• Each concept in a partitive concept system covers a range of instances that are not found in any other concept in the system.  There is no overlap of instances among the concepts in the system, unlike the generic type of concept system.  
• There is no relation between extension and intension of the concepts in the system.  Each concept has characteristics, none of which apply to the system as a whole.

I think that understanding different types of concept system has been overlooked by data modelers. Presumably this is because the arrangement of boxes and lines in a data model does not look very different for a generic or a partitive concept system.