Data-Brain Modeling for Systematic Brain Informatics - Brain Informatics - page 191

Algorithm 1. Conceptual View Construction
Input: Core and FD .
Output: CV .
1. Initialize empty concept sets CV.CF , CV.CFIC and CV.RF ;
2. Set CV.Core = Core ;
3. Set CV.R = { “ parentClassOf ”,“ includes - in - function ”,“ related - to - in - function ” } ;
4. CV.CF = TDR ( Core, “ parentClassOf ” ,∞,FD );
5. CV.CFIC = TDR ( Core, “ includes - in - function ” , ∞,FD );
6. CV.RF = TDR ( Core, “ related - to - in - function ” ,∞,FD );
7. return CV
Algorithm 2. Getting Traversal Directive Result: TDR
Input: C st , R , n ,and O .
Output: Concepts .
1. Initialize an empty set of result concepts, Concepts ;
2. Initialize the depth of the traversal, depth = n ;
3. If ( depth == 0) then
4. return Concepts ;
5. depth next = depth ;
6. If ( depth next <> ∞ )then
7. depth next = depth next − 1;
8. If ( R == “ parentClassOf ”) then
9. For each class c i in O
10. If ( c i subClassOf C st )then
11. Add c i into Concepts ;
12. Add TDR ( c i ,R,depth next ,O )into Concepts ;
13. End If
14. End For
15.Else
16. For each Restriction in C st
17. If ( Restriction is a value constraint and its property name == R )
then
18. c i = Range of Restriction ;
19. Add c i into Concepts ;
20. Add TDR ( c i ,R,depth next ,O )into Concepts ;
21. End If
22. End For
23.End If
24.return Concepts
- RF = < Core, < related - to - in - function,∞ >> ( FD ) is a specialization
of TD ( O ) and represents the concept set of related cognitive functions in a
conceptual view, where “ related - to - in - function ” is a relation in the function
dimension and used to describe the functional pertinence among cognitive
functions