Knowledge representation method based on causal
A knowledge representation and knowledge technology, applied in the field of computer artificial intelligence to facilitate machine learning
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example 1
[0043] Example 1: A=K1+K2 (K1 and K2 are both information, they form information A)
[0044] A*B=C→ D, E, F, ... (basic representation)
[0045] (K1+K2)*B=C→ D, E, F, ... (substituting the obtained knowledge representation)
example 2
[0046] Example 2: B=M1+M2+M3 (M1, M2, M3 are all information, they form information B)
[0047] A*B=C→ D, E, F, ... (basic representation)
[0048] A*(M1+M2+M3)=C→D, E, F, ... (substituting the obtained knowledge representation)
example 3
[0049] Example 3: G*H=A (main part of the first knowledge)
[0050] A*B=C (the main part of the second knowledge)
[0051] (G*H) *B=C (representation formed by substituting the former body of knowledge into the latter one)
[0052] G*H*B=C (another representation formed by substituting the former knowledge subject into the latter one)
[0053] The knowledge parameters synthesized in this example need to be determined according to the actual situation, so it is temporarily ignored here.
[0054] The substitution method is not limited to the above three forms, because there are many situations, I will not list them one by one here.
[0055] Merging method is a method of putting several knowledge subjects together and using a set of parameters. Usually the knowledge put together has some kind of connection. Examples are as follows:
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