Mutable relationships with flat categorization

I am going to talk a little about the way I have chosen to structure the dataset. The relationships between a primary concept and a dependent attribute only nest by one degree, and this level when interpreted by the software is flattened all the way to 0 levels of categorization. This has a benefit... it may seem abstract but it is a system after all. the short of it is something like this example

fire truck --has--> red --has--> apple

There is a relationship between fire truck and apple through the dependent attribute of red. This simple example of a compounded subject complement illustrates the basic unit of relationship the software I am developing produces. The value of this for the person working in a creative environment is that through the use of organizing relationships with this type of dataset unknown, tacitly known, and forgotten relational possibilities can be exploited.

The dataset is very simply written into a plain text file like this.

fire truck,red,wheels,firemen,truck,vehicle,combustion engine,(etc)

Here the primary concept is the first entry in the comma delimited string. Each line entry in the dataset starts with a newly categorized entry as a primary concept and given dependent attributes.

My intent in structuring the data and the file in this way is to incorporate a highlevel of ease of use, this way once the program is installed it is simple to maintain and note taking in this style is some what intuitive. I have stayed away from a XML scheme for structuring relationships because of its added complexity and the lack of need for more than one level of nested relationship.