Academic Search is a monthly indexing service. It was first published in 1997 by EBSCO Publishing in Ipswich, Massachusetts . Its academic focus is international universities , covering social science , education , psychology , and other subjects. Publishing formats covered are academic journals , magazines , newspapers , and CD-ROM .
31-568: Academic Search Complete was first published in 2007 as Academic Premier. It is an indexing and abstracting service, accessible via the World Wide Web . Coverage includes more than 8,500 full-text periodicals, including more than 7,300 peer-reviewed journals. In addition to full text, Academic Complete offers indexing and abstracts for more than 10,100 journals and a total of more than 10,600 publications including monographs , reports, conference proceedings, among others. Although coverage
62-548: A search index . Common words like articles (a, an, the) and conjunctions (and, or, but) are not treated as keywords because it's inefficient. Almost every English-language site on the Internet has the article " the ", and so it makes no sense to search for it. The most popular search engine, Google removed stop words such as "the" and "a" from its indexes for several years, but then re-introduced them, making certain types of precise search possible again. The term "descriptor"
93-487: A collection such as a library; and documents (such as books and articles) within a field of knowledge. Subject indexing is used in information retrieval especially to create bibliographic indexes 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. The process of indexing begins with any analysis of
124-415: A concept is discussed but is not identified in the text by an indexable keyword. Since this process is based on simple string matching and involves no intellectual analysis, the resulting product is more appropriately known as a concordance than an index. An alternative is assignment indexing where index terms are taken from a controlled vocabulary. This has the advantage of controlling for synonyms as
155-435: A loss of precision in comparison to pre-coordination. Indexers must make decisions about what entries should be included and how many entries an index should incorporate. The depth of indexing describes the thoroughness of the indexing process with reference to exhaustivity and specificity. An exhaustive index is one which lists all possible index terms. Greater exhaustivity gives a higher recall , or more likelihood of all
186-554: A paper's contribution to knowledge and index it accordingly. Or, in the words of Hjørland (1992, 1997) to index its informative potentials. "In order to achieve good consistent indexing, the indexer must have a thorough appreciation of the structure of the subject and the nature of the contribution that the document is making to the advancement of knowledge" (Rowley & Farrow, 2000, p. 99). Keyword (search) In information retrieval , an index term (also known as subject term , subject heading , descriptor , or keyword )
217-419: A relative frequency approach where frequency of a word in a document is compared to frequency in the database as a whole. Therefore, a term that occurs more often in a document than might be expected based on the rest of the database could then be used as an index term, and terms that occur equally frequently throughout will be excluded. Another problem with automated extraction is that it does not recognize when
248-444: A word or phrase from the search, getting rid of any results that include it. Multiple words can also be enclosed in quotation marks to turn the individual index terms into a specific index phrase . These modifiers and methods all help to refine search terms, to better maximize the accuracy of search results. Author keywords are an integral part of literature. Many journals and databases provide access to index terms made by authors of
279-436: Is a term that captures the essence of the topic of a document. Index terms make up a controlled vocabulary for use in bibliographic records . They are an integral part of bibliographic control , which is the function by which libraries collect, organize and disseminate documents. They are used as keywords to retrieve documents in an information system, for instance, a catalog or a search engine . A popular form of keywords on
310-559: Is from 1965 to the present, PDF back-file content coverage is from 1887. Subject areas covered include: animal science , anthropology , area studies , astronomy, biology, chemistry, civil engineering, electrical engineering, ethnic and multicultural studies , food science including related technology, general science, geography, geology, law, materials science , mathematics, mechanical engineering , music, pharmaceutical sciences , physics, psychology, religion and theology, veterinary science, women's studies, zoology and other fields. It
341-497: Is not easily compared to the cost of hardware, software and labor to manufacture a comparable set of full-text, fully searchable materials. With new web applications that allow every user to annotate documents, social tagging has gained popularity especially in the Web. One application of indexing, the book index , remains relatively unchanged despite the information revolution . Extraction indexing involves taking words directly from
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#1732801046904372-482: Is possible to extract commonly occurring phrases, it becomes more difficult if key concepts are inconsistently worded in phrases. Automated extraction indexing also has the problem that, even with use of a stop-list to remove common words, some frequent words may not be useful for allowing discrimination between documents. For example, the term glucose is likely to occur frequently in any document related to diabetes. Therefore, use of this term would likely return most or all
403-415: Is the act of describing or classifying a document by index terms , keywords, or other symbols in order to indicate what different documents are about , to summarize their contents or to increase findability . In other words, it is about identifying and describing the subject of documents. Indexes are constructed, separately, on three distinct levels: terms in a document such as a book; objects in
434-449: Is to decide on the subject matter of the document. In manual indexing, the indexer would consider the subject matter in terms of answer to a set of questions such as "Does the document deal with a specific product, condition or phenomenon?". As the analysis is influenced by the knowledge and experience of the indexer, it follows that two indexers may analyze the content differently and so come up with different index terms. This will impact on
465-425: Is to present the entries in a systematic order. This may involve linking entries. In a pre-coordinated index the indexer determines the order in which terms are linked in an entry by considering how a user may formulate their search. In a post-coordinated index, the entries are presented singly and the user can link the entries through searches, most commonly carried out by computer software. Post-coordination results in
496-527: Is updated on a daily basis. Field searches include full text articles (references can be included), academic journal titles, author, publication dates, abstracts, summations, cited references, and relevant images. Article results can also include thumbnail images. [REDACTED] This article incorporates public domain material from websites or documents of the United States Government . Subject indexing Subject indexing
527-468: The case, a keyword can be any term that exists within the document. However, priority is given to words that occur in the title, words that recur numerous times, and words that are explicitly assigned as keywords within the coding. Index terms can be further refined using Boolean operators such as "AND, OR, NOT." "AND" is normally unnecessary as most search engines infer it. "OR" will search for results with one search term or another or both. "NOT" eliminates
558-587: The document or assigning from a controlled vocabulary . 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. Subject indexing and its experts, professional indexers, catalogers , and librarians , remains crucial to information organization and retrieval. These experts understand controlled vocabularies and are able to find information that cannot be located by full text search. The cost of expert analysis to create subject indexing
589-444: The document such as the title, abstract, summary and conclusions, as analyzing the full text in depth is costly and time-consuming. An automated system takes away the time limit and allows the entire document to be analyzed, but also has the option to be directed to particular parts of the document. The second stage of indexing involves the translation of the subject analysis into a set of index terms . This can involve extracting from
620-473: The document. It uses natural language and lends itself well to automated techniques where word frequencies are calculated and those with a frequency over a pre-determined threshold are used as index terms. A stop-list containing common words (such as "the", "and") would be referred to and such stop words would be excluded as index terms. Automated extraction indexing may lead to loss of meaning of terms by indexing single words as opposed to phrases. Although it
651-540: The documents in the database. Post-coordinated indexing where terms are combined at the time of searching would reduce this effect but the onus would be on the searcher to link appropriate terms as opposed to the information professional. In addition terms that occur infrequently may be highly significant for example a new drug may be mentioned infrequently but the novelty of the subject makes any reference significant. One method for allowing rarer terms to be included and common words to be excluded by automated techniques would be
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#1732801046904682-498: The index terms match the topics they represent An index is said to be specific if the indexer uses parallel descriptors to the concept of the document and reflects the concepts precisely. Specificity tends to increase with exhaustivity as the more terms you include, the narrower those terms will be. Hjørland (2011) found that theories of indexing are at the deepest level connected to different theories of knowledge: The core of indexing is, as stated by Rowley and Farrow to evaluate
713-427: The other end of the scale, in a selective index only the most important aspects are covered. Recall is reduced in a selective index as if an indexer does not include enough terms, a highly relevant article may be overlooked. Therefore, indexers should strive for a balance and consider what the document may be used. They may also have to consider the implications of time and expense. The specificity describes how closely
744-561: The preferred term is indexed and synonyms or related terms direct the user to the preferred term. This means the user can find articles regardless of the specific term used by the author and saves the user from having to know and check all possible synonyms. It also removes any confusion caused by homographs by inclusion of a qualifying term. A third advantage is that it allows the linking of related terms whether they are linked by hierarchy or association, e.g. an index entry for an oral medication may list other oral medications as related terms on
775-428: The relevant articles being retrieved, however, this occurs at the expense of precision . This means that the user may retrieve a larger number of irrelevant documents or documents which only deal with the subject in little depth. In a manual system a greater level of exhaustivity brings with it a greater cost as more man-hours are required. The additional time taken in an automated system would be much less significant. At
806-407: The respective articles. How qualified the provider is decides the quality of both indexer-provided index terms and author-provided index terms. The quality of these two types of index terms is of research interest, particularly in relation to information retrieval . In general, an author will have difficulty providing indexing terms that characterize his or her document relative to other documents in
837-402: The same level of the hierarchy but would also link to broader terms such as treatment. Assignment indexing is used in manual indexing to improve inter-indexer consistency as different indexers will have a controlled set of terms to choose from. Controlled vocabularies do not completely remove inconsistencies as two indexers may still interpret the subject differently. The final phase of indexing
868-434: The subject of the document. The indexer must then identify terms which appropriately identify the subject either by extracting words directly from the document or assigning words from a controlled vocabulary . The terms in the index are then presented in a systematic order. Indexers must decide how many terms to include and how specific the terms should be. Together this gives a depth of indexing. The first step in indexing
899-549: The success of retrieval. Automatic indexing follows set processes of analyzing frequencies of word patterns and comparing results to other documents in order to assign to subject categories. This requires no understanding of the material being indexed. This leads to more uniform indexing but at the expense of the true meaning being interpreted. A computer program will not understand the meaning of statements and may therefore fail to assign some relevant terms or assign incorrectly. Human indexers focus their attention on certain parts of
930-433: The web are tags , which are directly visible and can be assigned by non-experts. Index terms can consist of a word, phrase, or alphanumerical term. They are created by analyzing the document either manually with subject indexing or automatically with automatic indexing or more sophisticated methods of keyword extraction. Index terms can either come from a controlled vocabulary or be freely assigned. Keywords are stored in
961-524: Was by Calvin Mooers in 1948. It is in particular used about a preferred term from a thesaurus . The Simple Knowledge Organization System language (SKOS) provides a way to express index terms with Resource Description Framework for use in the context of the Semantic Web . Most web search engines are designed to search for words anywhere in a document—the title, the body, and so on. This being