Résumé
Formal data is supported by means of specific languages from which the syntax and semantics have to be mastered, which represents an obstacle for collective intelligence. In contrast, informal knowledge relies on weak/ambiguous contributions e.g., I like. Reconciling the two forms of knowledge is a big challenge. We propose a brain-inspired knowledge representation approach called ViewpointS where formal data and informal contributions are merged into an adaptive knowledge graph which is then topologically, rather than logically, explored and assessed. We firstly illustrate within a mock-up simulation, where the hypothesis of knowledge emerging from preference dissemination is positively tested. Then we use a real-life web dataset (MovieLens) that mixes formal data about movies with user ratings. Our results show that ViewpointS is a relevant, generic and powerful innovative approach to capture and reconcile formal and informal knowledge and enable collective intelligence.