Information Filtration
Imagine the situation where a scientific article has only two percent of information necessary for a scientist. In order to find that information scientist is forced to read a lot of information that is not needed. Filtration function allows to decrease the amount of information in a precisely controllable manner and leave only what is necessary for a reader.
The principle. Green and red elements represent scientific facts. Lines that connect these elements indicate the relationship between the facts. Notice that a non-filtered diagram has a lot of elements. If we choose to filter that diagram by B (red elements), filtration will leave only those elements that are associated directly with B, other information will be excluded (see Filtered diagram). The key takeaway is that if the information is stored as a system, we can decrease the amount of information and choose what to read and what to exclude. To accomplish this with the standard text would be very hard.
The example. Figure on the left shows non-filtered diagram. Figure on the right shows a filtered diagram by element type “Problem” (red elements in the images). Filtered diagram has significantly fewer elements. Therefore, using filtration scientist can decrease the amount of information based on requirements and read what is necessary to him at that moment.
Information filtration is a fundamental feature of the Science Architect technology. It plays a synergistic role with information merge, where the combination of both of these features enables the possibility to have automatic knowledge construction feature.