Skip to main content

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.
Labels:

Comments

Post a Comment

Popular Posts

Create Your Own Social Networking Site

Create Your Own Social Networking Site JCOW: Ethical Hacking Top 10 reasons to choose Jcow:- 1. Handle more traffic - Clean codes and Dynamic caching can lower the CPU load and  speed up your website. 2 Make your site more interactive - Well designed Jcow applications help you members to connect and communicate with others more effectively. 3 Add questions to the Registration Form - You can add new member fields, which will be displayed to the registration form, profile form, and the member browsing form. 4 Easily share stuff - Within the AJAX sharing Box, your members can publish status,  photos, videos, and blogs. 5 Customize and Extend your Jcow Network - A Jcow network consists of core apps(like "Friends" and "Messages") and optional apps(like "Blogs" and ""Videos"). You can enable/disable optional apps. You can also develop your own apps. 6 Every profile could be Unique - Members can customize their own profile theme and  add music play...
Post a Comment
Read more

The Problem of Abstraction in Definitions of Data Objects

I think there is a major problem in not being able to understand and work with different levels of abstraction.  By "abstraction" in this sense I mean one concept system that somehow describes or defines (not merely relates to) another concept system.  I think this is a big problem for definitions in data models. Let us take an example in a retail business such as mortgage banking: Customer Name.  Customer Name exists in the business.  They use it all the time.  Maybe it is sometimes called Borrower Name, but the concept is the same.  This is the Level 1 abstraction. Now let us think of data values in a column in a table that holds Customer Name.  These data values are stored as a code of 1's and 0's.  Of course these bits are rendered into something we can read.  However, this is not the same as the Customer Name in the business.  I worked for a place where they prefixed the name of anyone who had recently left with "ZZZ"....
Post a Comment
Read more

The Humpty-Dumpty Principle in Definitions

In dealing with empty concepts, we came across the issue that if somebody uses a term that potentially has an unintelligible definition, they are likely to defend themselves by quickly making up some kind of definition.   I strongly suspect that in such cases, the usage of the term will be inconsistent with the definition.  Which brings us to Humpty-Dumpty. Lewis Carroll (Rev. Charles Lutwidge Dodgson) is best known for his children's' books Alice's Adventures in Wonderland and Through the Looking Glass .  It is in the latter that Humpty Dumpty - an argumentative egg perched on a wall has the following exchange with Alice: 'And only one for birthday presents, you know. There's glory for you!' `I don't know what you mean by "glory",' Alice said. Humpty Dumpty smiled contemptuously. `Of course you don't -- till I tell you. I meant "there's a nice knock-down argument for you!"' `But "glory" doesn't mean "...
Post a Comment
Read more