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Controlled vocabulary

Controlled vocabularies provide a way to organize knowledge for subsequent retrieval. They are used in knowledge organization systems. Controlled vocabulary schemes mandate the use of predefined, authorised terms that have been preselected by the designer of the vocabulary, in contrast to natural language vocabularies, where there is no restriction on the vocabulary.


  • In library and information science 1
  • Indexing languages 2
  • Applications 3
  • See also 4
  • References 5
  • External links 6

In library and information science

In library and information science controlled vocabulary is a carefully selected list of words and phrases, which are used to tag units of information (document or work) so that they may be more easily retrieved by a search. Controlled vocabularies solve the problems of homographs, synonyms and polysemes by a bijection between concepts and authorized terms. In short, controlled vocabularies reduce ambiguity inherent in normal human languages where the same concept can be given different names and ensure consistency.

For example, in the Library of Congress Subject Headings (a subject heading system that uses a controlled vocabulary), authorized terms—subject headings in this case—have to be chosen to handle choices between variant spellings of the same concept (American versus British), choice among scientific and popular terms (Cockroaches versus Periplaneta americana), and choices between synonyms (automobile versus cars), among other difficult issues.

Choices of authorized terms are based on the principles of user warrant (what terms users are likely to use), literary warrant (what terms are generally used in the literature and documents), and structural warrant (terms chosen by considering the structure, scope of the controlled vocabulary).

Controlled vocabularies also typically handle the problem of homographs, with qualifiers. For example, the term "pool" has to be qualified to refer to either swimming pool, or the game pool to ensure that each authorized term or heading refers to only one concept.

There are two main kinds of controlled vocabulary tools used in libraries: subject headings and thesauri. While the differences between the two are diminishing, there are still some minor differences.

Historically subject headings were designed to describe books in library catalogs by catalogers while thesauri were used by indexers to apply index terms to documents and articles. Subject headings tend to be broader in scope describing whole books, while thesauri tend to be more specialized covering very specific disciplines. Also because of the card catalog system, subject headings tend to have terms that are in indirect order (though with the rise of automated systems this is being removed), while thesaurus terms are always in direct order. Subject headings also tend to use more pre-coordination of terms such that the designer of the controlled vocabulary will combine various concepts together to form one authorized subject heading. (e.g., children and terrorism) while thesauri tend to use singular direct terms. Lastly thesauri list not only equivalent terms but also narrower, broader terms and related terms among various authorized and non-authorized terms, while historically most subject headings did not.

For example, the Library of Congress Subject Heading itself did not have much syndetic structure until 1943, and it was not until 1985 when it began to adopt the thesauri type term "Broader term" and "Narrow term".

The Library of Congress system, MeSH, and Sears. Well known thesauri include the Art and Architecture Thesaurus and the ERIC Thesaurus.

Choosing authorized terms to be used is a tricky business, besides the areas already considered above, the designer has to consider the specificity of the term chosen, whether to use direct entry, inter consistency and stability of the language. Lastly the amount of pre-co-ordinate (in which case the degree of enumeration versus synthesis becomes an issue) and post co-ordinate in the system is another important issue.

Controlled vocabulary elements (terms/phrases) employed as tags, to aid in the content identification process of documents, or other information system entities (e.g. DBMS, Web Services) qualifies as metadata.

Indexing languages

There are three main types of indexing languages.

  • Controlled indexing language - Only approved terms can be used by the indexer to describe the document
  • Natural language indexing language - Any term from the document in question can be used to describe the document.
  • Free indexing language - Any term (not only from the document) can be used to describe the document.

When indexing a document, the indexer also has to choose the level of indexing exhaustivity, the level of detail in which the document is described. For example, using low indexing exhaustivity, minor aspects of the work will not be described with index terms. In general the higher the indexing exhaustivity, the more terms indexed for each document.

In recent years free text search as a means of access to documents has become popular. This involves using natural language indexing with an indexing exhaustively set to maximum (every word in the text is indexed). Many studies have been done to compare the efficiency and effectiveness of free text searches against documents that have been indexed by experts using a few well chosen controlled vocabulary descriptors.



In some cases controlled vocabulary can enhance recall as well, because unlike natural language schemes, once the correct authorized term is searched, you don't need to worry about searching for other terms that might be synonyms of that term.

However, a controlled vocabulary search may also lead to unsatisfactory recall, in that it will fail to retrieve some documents that are actually relevant to the search question.

This is particularly problematic when the search question involves terms that are sufficiently tangential to the subject area such that the indexer might have decided to tag it using a different term (but the searcher might consider the same). Essentially, this can be avoided only by an experienced user of controlled vocabulary whose understanding of the vocabulary coincides with the way it is used by the indexer.

Another possibility is that the article is just not tagged by the indexer because indexing exhaustivity is low. For example, an article might mention football as a secondary focus, and the indexer might decide not to tag it with "football" because it is not important enough compared to the main focus. But it turns out that for the searcher that article is relevant and hence recall fails. A free text search would automatically pick up that article regardless.

On the other hand, free text searches have high exhaustivity (you search on every word) so it has potential for high recall (assuming you solve the problems of synonyms by entering every combination) but will have much lower precision.

Controlled vocabularies are also quickly out-dated and in fast developing fields of knowledge, the authorized terms available might not be available if they are not updated regularly. Even in the best case scenario, controlled language is often not as specific as using the words of the text itself. Indexers trying to choose the appropriate index terms might misinterpret the author, while a free text search is in no danger of doing so, because it uses the author's own words.

The use of controlled vocabularies can be costly compared to free text searches because human experts or expensive automated systems are necessary to index each entry. Furthermore, the user has to be familiar with the controlled vocabulary scheme to make best use of the system. But as already mentioned, the control of synonyms, homographs can help increase precision.




In large organizations, controlled vocabularies may be introduced to improve organization instead of slightly different ones to refer to the same thing.

Web searching could be dramatically improved by the development of a controlled vocabulary for describing Web pages; the use of such a vocabulary could culminate in a Semantic Web, in which the content of Web pages is described using a machine-readable metadata scheme. One of the first proposals for such a scheme is the Dublin Core Initiative. An example of a controlled vocabulary which is usable for indexing web pages is PSH.


Controlled vocabularies of the

  • — explains how controlled vocabularies are useful in describing images and information for classifying content in electronic databases.
  • — useful guides to creating and editing your own controlled vocabulary suitable for image cataloging.

External links

  1. ^ Amy Warner, A taxonomy primer.
  2. ^ Karl Fast, Fred Leise and Mike Steckel, [5]
  3. ^ Cory Doctorow, Metacrap.
  4. ^ Mark Pilgrim, eXchangeable Faceted Metadata Language.
  6. Controlled Vocabularies Links to examples of thesauri and classification schemes used in the domain of Agriculture, Fisheries, Forestry etc.
  1. ^ "The Person vocabulary of". Retrieved 13 March 2015. 
  2. ^ "The Book vocabulary of". Retrieved 13 March 2015. 
  3. ^ "Dublin Core Metadata Element Set, Version 1.1". Retrieved 13 March 2015. 
  4. ^ "The Event vocabulary of". Retrieved 13 March 2015. 


See also

To use machine-readable terms from any controlled vocabulary, web designers can choose from a variety of annotation formats, including RDFa, HTML5 Microdata, or JSON-LD in the markup, or RDF serializations (RDF/XML, Turtle, N3, TriG, TriX) in external files.

and so on. [4]

