Describe the mechanism of Knowledge discovery in databases

Knowledge discovery in databases (KDD) revolves around the investigation and creation of knowledge, processes, algorithms, and the mechanisms for retrieving potential knowledge from data collections." [Norton, 1999]. "Classification schemes have properties that enable the representation of entities and relationships in structures that reflect knowledge of the domain being classified." In this context, hierarchies, trees, paradigms, and faceted classification can reflect structures, discover, and create new knowledge [Kwasnik, 1999]. How to find interesting previously unknown implicit information in scientific literature is examined in the paper "Implicit text linkages between medicine records: using Arrowsmith as an aid to scientific discovery" by Swanson and Smallheiser (1999). "Extracting hidden knowledge from humanities databases is especially problematic because the literature, written in 'everyday' rather than technical language, lacks the precision required for efficient retrieval, and because humanities scholars seek new analogies rather than causes...an illuminating new humanities analogy was found by constructing a search statement in which proper names were coupled with associated concepts [Cory, 1977]. Small (1999) presents a "methodology for creating pathways through the scientific literature following co-citation links."