FAST (Faceted Application of Subject Terminology)
FAST (Faceted Application of Subject Terminology) is derived from the Library of Congress Subject Headings (LCSH), one of the library domain’s most widely used subject terminology schemas. The development of FAST has been a collaboration of OCLC Research and the Library of Congress. Work on FAST began in late 1998.
FAST has been developed in large part to meet the need for a general-use subject terminology scheme, which is:
- simple to learn and apply,
- faceted-navigation-friendly, and
- modern in its design.
The broad purpose of adapting the LCSH with a simplified syntax to create FAST is to retain the very rich vocabulary of LCSH while making the schema easier to understand, control, apply, and use. The schema maintains upward compatibility with LCSH, and any valid set of LC subject headings can be converted to FAST headings.
FAST is a nine-facet vocabulary with a universe of approximately 1.8 million headings across all facets. The facets are designed to be used in tandem, but each may also be used independently. The rules of application are very simple.
With the rapid growth of digital information, came a need for a simplified indexing schema, which could be assigned and used by non-professional catalogers or indexers.
The origin of FAST can be traced to observations by OCLC Research staff involved with the OCLC Cooperative Online Resource Catalog (CORC) project, which focused on the cataloging of web resources. CORC participants typically wanted to be able to adopt simple, low-cost, low-effort approaches to describing web resources (e.g., using Dublin Core). In the course of the CORC project, it became clear that a significant barrier to minimal-effort resource description was the lack of an easy-to-learn and apply general subject vocabulary.
Additionally, work during the same time period by the Subcommittee on Metadata and Subject Analysis of the Association for Library Collections and Technical Services’ Subject Access Committee identified specific functional requirements of subject data in the metadata record (ALCTS 1999), and these requirements mapped well to the intended outcomes of what would become the FAST project.
A family of nine modular, complementary vocabularies designed to support faceted retrieval, FAST represents a well-designed, professionally stewarded controlled vocabulary set that carries a modest initial training burden and operational overhead comparable to keyword indexing. This combination of attributes, along with a design and implementation that make FAST well-suited for linked data applications, provide a viable and far superior alternative to key word indexing or other uncontrolled approaches.
FAST is used by a variety of libraries and other organizations to provide subject indexing of print and digital resources.
In developing FAST, the primary objectives were (1) compatibility with existing metadata, (2) ease of assignment, (3) retrieval effectiveness, (4) cost of maintenance, and (5) semantic interoperability. The development team determined that these objectives could best be satisfied by a fully enumerative faceted subject heading schema derived from the Library of Congress Subject Headings.
The individual terms in the FAST vocabulary are divided into nine distinct categories or facets: Personal names, Corporate names, Meeting names, Geographic names, Events, Titles, Time periods, Topics, and Form/Genre.
As a fully enumerative system, all subject headings are established with authority records eliminating the need to synthesize headings according to a complex set of syntax rules.
The FAST authority file contains over 1,800,000 authority records
Quick Start Guide
The FAST Quick Start Guide, prepared by the FAST Policy and Outreach Committee in 2022 is intended to aid anyone wishing to use FAST as a subject vocabulary.
- Bryan Baldus
- Rick Bennett
- Rebecca Dean
- Shanna Griffith
- Kerre Kammerer
- Michael Phillips
- Russell Schelby
- Cynthia Whitacre
Former Team Members
- Robert Bremer
- Eric Childress
- Kay Clopton
- Edward T. O'Neill
- Jeff Mixter
- Lois Mai Chan (University of Kentucky)
- Chris Stanton
- Diane Vizine-Goetz
Comments or Questions?