Prof.dr. B. Mons / quote 5 of 5
Knowledge discovery in escience
Each Knowlet has an ‘attraction’ to each other Knowlet with similar concepts in its ‘Object Cloud’, while some knowlets have very little to do with each other and may even repulse each other, some others may move closer and close in the Concept Web, pushed and pulled by many other knowlets in the space. The Knowlet of Malaria may move so close to the Knowlets of the cancer drug Tegafur that, even though they have never been mentioned together before, the Social Machine will hypothesise a potential anti-malaria role for the drug. [...] eScience needs different complementary levels of reasoning. Think of the metaphor of the helicopter view. One would never see the abbarent growth pattern in a cornfield caused by the remains of a Roman fortress when walking in the midst of the field. However, after spotting the pattern from the helicopter, one needs to land, take a shovel and dig to find the ruines. Next step would be the laboratory experiments to demonstrate the age of the stones before the conclusion can be drawn that indeed the pattern observed revealed a Roman fortress. Knowlets enable the helicopter view. With for instance Description Logics the immediate surroundings of the new associations can be explored (compared to the shovel), whilst final comfirmation of causal biological relationships in the wet Lab will follow.