Your Home for Faceted Classification Tools
Facetmap is both a data model and a software package, created to let users browse complex metadata while retaining a simple, familiar, menu interface.

Glossary: Faceted Classification Terminology

  • Facet: a group of headings which all define a certain method of classification. That is, a facet is a way in which a resource can be classified; for example, classified by color, classified by geography, classified by subject, etc. The wine demo uses three facets: Varietal, Region, and Price.
  • Faceted Classification: In a strict faceted classification model, a resource is classified under one heading from each facet that applies to it. A resource does not have to be classified at all in a given facet, if that facet's method of classification doesn't apply to the resource. The wine demo uses the classification model, which is why one wine cannot be from two regions, have two prices, etc.

  • Heading: A classification that any given resource may have. In the wine demo, the heading "White Zinfandel" (in the facet "Varietal") applies to the Beringer 2000 White Zinfandel, and applies to other wines as well.

  • Range: A different type of facet which expresses nondiscrete numeric headings such as price, date, number of pageviews, and so on. Its headings can be any value within a predefined range of values, and the user is usually free to select any smaller range of values within this facet to narrow his selection.

  • Resource: An object (document, person, consumer item, etc.) you want to find by navigating a FacetMap. A resource falls under one or more headings, which is how users find it. In the wine demo, bottles of wine are the resources.

  • Taxonomy: A type of facet in which the headings are arranged into a hierarchy. This is the most broadly useful type of facet, since thousands of headings can be organized into a presentable selection.

  • Tagging: This is a loose categorization model in which a resource may be tagged with any number of headings from each facet. Why is this so different? See Strict Faceted Classification.