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Conceptual analysis of Web metadata [Kent & Neuss 1994] [Neuss & Kent 1995] provides a formal mechanism for
the automatic categorization and classification of documents. By applying conceptual scales to metadata, faceted
conceptual spaces can be constructed. In these concept spaces, various properties collocate Web document-like objects
according to common characteristics. Conceptual scaling is an interpretative act with a highly interactive user
interface component. This paper advocates a method called conceptual browsing as a navigation and structural
browsing technique. It is based on the notion of “conceptual distance,” a formal similarity metric for documents. The
algorithmic definition of similarity allows for an incremental browsing of the conceptual vicinity of a document or a
class of documents.
Conceptual Space
Conceptual Knowledge Processing
Conceptual knowledge processing [Wille 1982] [Ganter & Wille 1989] is a relatively new discipline arising out of the
mathematical theory of lattices and the calculus of binary relations. It is closely related to the areas of knowledge
representation in computer science and cognitive psychology. Conceptual knowledge processing provides for the
automatic classification of both knowledge and documents via representation of a user's faculty for interpretation as
encoded in conceptual scales. Such conceptual scales correspond to the facets of synthetic classification schemes, such
as Ranganathan's Colon classification scheme, in library science. Conceptual knowledge processing uses objects,
attributes and formal concepts as its basic constituents. Objects and attributes are connected through has-a incidence
relationships, while formal concepts are connected through is-a subtype relationships. In many contexts appropriate
for Web resources, the objects are document-like objects and the attributes are properties of those document-like
objects which are of interest to the Web user. Conceptual knowledge processing uses formal concepts as its central
notion and uses concept lattices as an approach to knowledge representation [Wille 1992]. A formal concept (also
called a conceptual class or category) consists of a collection of entities or objects exhibiting one or more common
characteristics, traits or attributes. Formal concepts are logically characterized by their extent and intent. The extent of
a concept is the aggregate of entities or objects which it includes or denotes. The intent of a concept is the sum of its
unique characteristics, traits or attributes, which, taken together, imply the formal concept. The process of
subordination of concepts and collocation of objects exhibits a natural order, proceeding top-down from the more
generalized concepts with larger extent and smaller intent to the more specialized concepts with smaller extent and
larger intent. This is-a relationship is a partial order called generalization-specialization. Concepts with this
generalization-specialization ordering form a class hierarchy called a concept lattice. This is the formal representation
for conceptual knowledge. The use of formal concepts as a conceptual structuring mechanism corresponds to the use
of similarity clusters in information retrieval, although foundationally, formal concepts are based more on logical
implication rather than a nearness notion.
A concept space is a named substructure of a concept lattice. In a precise mathematical sense, a concept space is a
“distributed formal concept.” The main purpose for the concept space notion is to provide for the organization and
customization by the user of their own information space. The representational mechanism of a concept space serves
as a firm foundation for the basic paradigms of Internet/intranet resource discovery and wide area information
management systems; both organization-navigation and search-retrieval [Kent & Bowman 1995]. The use of
conceptual space is a natural outgrowth of the original conceptual knowledge processing approach for structuring and
organizing the networked information resources in the World Wide Web [Kent & Neuss 1994]. Conceptual space
forms its naming mechanism via conceptual views. A conceptual view is a name for a formal concept within a concept
lattice. Conceptual views are created by any of the following methods: (1) view definitions in terms of meets and joins
of other views, meets of attributes, and joins of objects; and (2) view definitions (virtual views) in terms of the meetvectors
of apposition concept spaces or the join-vectors of subposition concept spaces. Virtual views have applications
in: (i) the interpretation by conceptual scaling; (ii) the parallel implementation of conceptual knowledge processing,
and (iii) collaboration in distributed information systems. Virtual meet views are useful in conceptual scaling when we
use various facets of information in order to organize a conceptual space over an information system. For
collaboration, we can merge remote public data interpretation, as represented by conceptual scaling, with local private
data interpretation. This provides one approach for the private customization and sharing of public data. According to