The growth of the Internet and the World Wide Web has brought about new models of interaction within newsgroups, discussion lists, web-pages, etc., which are defining new collective social practices. The e-communities emerging from these practices have generated new needs in terms of knowledge management and processing. This paper explores the problem of knowledge acquisition and modeling of e-community interactions using the topic map standard. The distributed and highly dynamic nature of the resources involved in these discussion forums pose a new range of problems in terms of Topic Map construction. One important issue is that such volatile and heterogeneous resources are made up of multiple diverging (and sometimes contradictory) points of view and thus cannot be described by a single semantic model. Our work here focuses on how to construct (and update) a navigable semantic structure from multiple, fragmentary viewpoints. We use inductive Natural Language Processing techniques to identify semantic classes on the basis of word associations patterns and to construct semantic dimensions which define the conceptual space of a viewpoint. These classes constitute potential topics in the resulting topic map. The associations among the topics are constrained to a certain conceptual space or scope. We show the advantages of modeling scopes as conceptual spaces based on a geometric structure. On the one hand, it provides a way to embed topics in an abstract n-dimensional description space where spatial relations between topics have a semantic interpretation. On the other hand, the topological structure of the space provides geometric constraints for comparing conceptual spaces and thus assessing the semantic convergences and divergences across different viewpoints. Finally, we show how our approach for identifying semantic patterns in disparate resources and organizing them in a topic map framework can be useful for such tasks as domain monitoring, technology watch, opinion tracking, etc.