Reconciling the ecosystem of semantic Web data with the ecosystem of social Web participation has been a major issue for the Web Science community. To answer this need, we propose an innovative approach called ViewpointS where the knowledge is topologically, rather than logically, explored and assessed. Both social contributions and linked data are represented by triples agent-resource-resource called “viewpoints”. A “viewpoint” is the subjective declaration by an agent (human or artificial) of some semantic proximity between two resources. Knowledge resources and viewpoints form a bipartite graph called “knowledge graph”. Information retrieval is processed on demand by choosing a user's “perspective” i.e., rules for quantifying and aggregating “viewpoints” which yield a “knowledge map”. This map is equipped with a topology: the more viewpoints between two given resources, the shorter the distance; moreover, the distances between resources evolve along time according to new viewpoints, in the metaphor of synapses' strengths. Our hypothesis is that these dynamics actualize an adaptive, actionable collective knowledge. We test our hypothesis with the MovieLens dataset by showing the ability of our formalism to unify the semantics issued from linked data e.g., movies' genres and the social Web e.g., users' ratings. Moreover, our results prove the relevance of the topological approach for assessing and comparing along the time the respective powers of 'genres' and 'ratings' for recommendation.