The rules used for the semantic analysis are, in themselves, an important form of knowledge as they encode the way in which the relationships between repository objects are inferred from their content by users of the repository. Some semantic information may be encoded as associations between topics or as topic-occurrence relationships. Other semantic information may be extracted which applies to just a single repository object and this information may be represented using a facet. When used to generate an index for a corpus, a semantic Topic Map provides indexing features above and beyond those of a standard static index. Facets provide easily searchable meta-data.
Associations provide rich, typed cross-linking between conceptual areas.
The User-Defined Topic Map provides an individual with a means of creating their own perspective on a set of data. The perspective adds value to the data by asserting associations between repository objects based on some deeper understanding of the concepts represented by those objects. The data is organised according to the user's work processes. Individual Workspaces Shared Workspaces Knowledge Management Individual workspaces Using a Topic Map to create an individual workspace gives the user a means of better managing access to frequently used documents and to organise data in multiple ways. Topic Maps can be used to create logical paths from an abstract concept to a specific document in a way which more closely matches the way the user thinks.