Modeling the Knowledge Base

On Brain, modeling is the graphical representation of knowledge, its relationships with other knowledges ( parents and children ) and the topics in which it is divided, all through a mental map. The modeling is part of the process of creating the knowledge base itself, and it is synchronized with the robot's real base.

Modeling the knowledge base of a chatbot is not exactly a trivial task, since there is a set of variables that directly affect the effectiveness of a good human-robot interaction, such as the richness of the vocabulary of both, or the clarity and objectivity of messages of either the specialization or generalization expected by an user on a particular subject, etc. Also certain subjects may be too complex or too large to be handled by a robot.

Considering that, Brain was designed to facilitate this task by adopting the strategy "divide and conquer". By dividing, the knowledge can be modeled with very fine granularity, being reduced to several small knowledges ( which we call "atomic knowledge" ) where each one is very specific, since it focuses only on a particular aspect of a greater knowledge. Some of the advantages of this approach is to have a broader view on knowledge and be much easier to define all the topics covered by it. The result of this approach is a much more solid base and complete knowledge.

But the most important for the base to be well modeled is that the botmaster master each knowledge he wants to model. Must already have very clear in his mind what he wants to represent through of Brain's mental map. Below is an outline of the modeling process:

1. Define a target audience ;
2. Define the subjects of interest to this target audience;
3. Define the scope of subjects ( knowledge to be modeled );
4. For each item of scope ( all knowledge ):
   1. Understand the knowledge that will be added;
   2. Define the relationships ( parents and children );
   3. Define the topics.