The present paper has sketched a general family of algorithms to extract meta-data about documents from the way these documents are consulted by users. Implementing such a system in a digital library would automatize much of the hard work that would otherwise need to be performed by highly trained information scientists.
However, the results of this system are envisaged to complement or support traditional methods rather than fully replace them. The reason is that the proposed system focuses on otherwise difficult to formalize properties of documents, namely the subjective associations that exist in the mind of the users between their different subjects and contents. The advantage is that these associations allow us to build a system that emulates human intuition, so that it can anticipate the desires of its users and provide them with the information they would find most interesting, even when these users cannot explicitly formulate what they are looking for. This is particularly useful for multimedia documents, which do not contain any searchable keywords, and for queries that are as yet illdefined.