A controlled vocabulary is a set of preselected terms from which a cataloger or indexer selects for assigning subject headings or descriptors to a work in a library catalog or bibliographic database. Vocabulary control ensures consistency in a catalog or databes and increases the efficiency of information retrieval by solving the problems of homographs, synonyms and polysemes of natural language.
Vocabulary control includes policies, procedures, and methodologies of term assignments and clarification of the semantic relationships among terms. The Library of Congress Subject Headings are an example of a controlled vocabulary.
In library and information science, a controlled vocabulary is a carefully selected list of words and phrases that are used to tag units of information (document or work) so that they may be more easily retrieved by a search. In Guidelines for the Construction, Format, and Management of Monolingual Controlled Vocabulary, NISO (National Information Standards Organization (U.S.) explains the purposes of vocabulary control:
The purpose of controlled vocabularies is to provide a means for organizing information. Through the process of assigning terms selected from controlled vocabularies to describe documents and other types of content objects, the materials are organized according to the various elements that have been chosen to describe them. Controlled vocabularies serve five purposes:
- Translation: Provide a means for converting the natural language of authors, indexers, and users into a vocabulary that can be used for indexing and retrieval.
- Consistency: Promote uniformity in term format and in the assignment of terms.
- Indication of relationships: Indicate semantic relationships among terms.
- Label and browse: Provide consistent and clear hierarchies in a navigation system to help users locate desired content objects.
- Retrieval: Serve as a searching aid in locating content objects.
Controlled vocabularies solve the problems of homographs, synonyms and polysemes by ensuring that each concept is described using only one authorized term and each authorized term in the controlled vocabulary describes only one concept. In short, controlled vocabularies reduce ambiguity inherent in normal human languages and ensure consistency.
For example, in the Library of Congress Subject Headings (a subject heading system that uses 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), scientific and popular terms (Cockroaches versus Periplaneta americana), and between synonyms (automobile versus cars) among other difficult choices.
Authorized terms are selected 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 thesauri terms are always in direct order. Subject headings also tend to use more pre-co-ordination 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 Headings did not have much syndetic structure until 1943, and it was not until 1985 when it began to adopt the thesauri type "Broader term" and "Narrow term."
The terms are chosen and organized by trained professionals (including librarians and information scientists) who possess expertise in the subject area. Controlled vocabulary terms can accurately describe what a given document is actually about, even if the terms themselves do not occur within the document's text. Well known subject heading systems are library of congress subject heading, MESH, Sears. Well known thesauri are Art and Architecture Thesaurus, ERIC Thesaurus etc.
Choosing authorized terms is a tricky business. Besides the issues already considered above, the designer has to consider the specificity of the term chosen, whether to use direct entry, and the 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 vocabularies used to tag documents are considered metadata.
Subject indexing is the act of describing a document by index terms to indicate what the document is about or to summarize its content. The index terms are often selected from some form of controlled vocabulary. Subject indexing is used in information retrieval especially to create Bibliographic databases to retrieve documents on a particular subject. Examples of academic indexing services are Zentralblatt MATH, Chemical Abstracts and PubMed. The index terms were mostly assigned by experts but author keywords are also common.
With the ability to conduct a full text search widely available, many people have come to rely on their own expertise in conducting information searches and full text search has become very popular. With new web applications that allow every user to tag documents, social tagging has gained popularity. However, subject indexing is done by professional indexers and librarians, and they remain crucial to information organization and retrieval. Indexers and Librarians understand controlled vocabularies and are able to find information that can't be located by full text search.
There are three main types of indexing languages.
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, more terms are 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.
Controlled vocabularies may improve the accuracy of free text searching, such as to reduce irrelevant items in the retrieval list. These irrelevant items (false positives) are often caused by the inherent ambiguity of natural language. For example, football is the name given to a number of different team sports. The most popular of these team sports also happens to be called soccer in several countries. The English language word football is also applied to Rugby football (Rugby union and rugby league), American football, Australian rules football, Gaelic football, and Canadian football. A search for football therefore will retrieve documents that are about several completely different sports. Controlled vocabulary solves this problem by tagging the documents in such a way that the ambiguities are eliminated.
Compared to free text searching, the use of a controlled vocabulary can dramatically increase the performance of an information retrieval system, if performance is measured by precision (the percentage of documents in the retrieval list that are actually relevant to the search topic).
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.
Controlled vocabularies are also quickly out-dated and in fast developing fields of knowledge, the authorized terms 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.
Numerous methodologies have been developed to assist in the creation of controlled vocabularies, including faceted classification, which enables a given data record or document to be described in multiple ways.
Controlled vocabularies, such as the Library of Congress Subject Headings, are essential components of bibliography, the study and classification of books. They were initially developed in library and information science. In the 1950s, government agencies began to develop controlled vocabularies for the burgeoning journal literature in specialized fields; an example is the Medical Subject Headings (MeSH) developed by the U.S. National Library of Medicine. Subsequently, for-profit firms (called Abstracting and indexing services) emerged to index the fast-growing literature in every field of knowledge. In the 1960s, an online bibliographic database industry developed one based on dialup X.25 networking. These services were seldom made available to the public because they were difficult to use; specialist librarians called search intermediaries handled the searching job. In the 1980s, the first full text databases appeared; these databases contain the full text of the index articles as well as the bibliographic information. Online bibliographic databases have migrated to the Internet and are now publicly available; however, most are proprietary and can be expensive to use. Students enrolled in colleges and universities may be able to access some of these services; some of these services may be accessible without charge at a public library.
In large organizations, controlled vocabularies may be introduced to improve technical communication. The use of controlled vocabulary ensures that everyone is using the same word to mean the same thing. This consistency of terms is one of the most important concepts in technical writing and knowledge management, where effort is expended to use the same word throughout a document or 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.
It is unlikely that a single metadata scheme will ever succeed in describing the content of the entire Web. To create a Semantic Web, it may be necessary to draw from two or more metadata systems to describe a Web page's contents. The eXchangeable Faceted Metadata Language (XFML) is designed to enable controlled vocabulary creators to publish and share metadata systems. XFML is designed on faceted classification principles.
All links retrieved March 22, 2017.
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