Classical databases and knowledge organization: A case for boolean retrieval and human decision‐making during searches

Abstract

This paper considers classical bibliographic databases based on the Boolean retrieval model (such as MEDLINE and PsycInfo). This model is challenged by modern search engines and information retrieval (IR) researchers, who often consider Boolean retrieval a less efficient approach. The paper examines this claim and argues for the continued value of Boolean systems, and suggests two further considerations: (a) the important role of human expertise in searching (expert searchers and "information literate" users) and (b) the role of library and information science and knowledge organization (KO) in the design and use of classical databases. An underlying issue is the kind of retrieval system for which one should aim. Warner's (2010) differentiation between the computer science traditions and an older libraryoriented tradition seems important; the former aim to transform queries automatically into (ranked) sets of relevant documents, whereas the latter aims to increase the "selection power" of users. The Boolean retrieval model is valuable in providing users with the power to make informed searches and have full control over what is found and what is not. These issues may have significant implications for the maintenance of information science and KO as research fields as well as for the information profession as a profession in its own right.

Which academic search systems are suitable for systematic reviews or meta‐analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources

“…It is striking that half the search systems we examined have at least some issues with Boolean queries. This is particularly unfortunate because Boolean searching is effective, especially for systematic search strategies: “medical research indicates that expert searching based on Boolean systems is still the most effective method [of searching].” ( , p1570)…”

“…These semantic search engines tend to be designed to reward exploratory rather than systematic search behavior. These tendencies add to the notion that “the problem is […] that an ideological tendency to make things ‘user friendly’ (and the market bigger) tends to hurt the development of systems aimed at increasing the selection power of users and search experts.” ( , p1570) Our findings indicating that these systems are inadequate to be used as principal systems in systematic searches support this notion. The criticism of user‐friendliness at any cost is especially directed at Google Scholar, which is more concerned with “ tuning ” its first results page ( , p15) than with overall precision.…”

“…Because evidence synthesis has very specific information needs determined by choices of constructs, methods, and research questions, it needs complex search strings to determine queries that link concepts of interest. Boolean operators have been shown to be essential for sampling in evidence synthesis as they “allow the searcher to use set theory to help define the items that will be retrieved by a search.” ( , p103) They provide “a great range of strategies are available to increase ‘recall’ and ‘precision.’” ( , p1570) We used a combination of a total of six terms—research, define, paper, Asterix, table, and analysis—to see whether the result set increased, decreased, or remained constant after adding one more term. For strings with OR operators the results set should increase or at least remain constant with each additional term, with AND operators, it should decrease or remain constant, and with NOT, it should decrease or remain constant.…”

“…A suitable search system provides good coverage in the specific area of interest and allows the user to specify a query with high precision and recall. Accordingly, search systems “should be evaluated against the background of what is found for—and what remains hidden from—the users.” ( , p1570)…”

Variation in number of hits for complex searches in Google Scholar

“…In traditional bibliographic databases, search strategies can typically be designed to retrieve all articles that meet certain criteria based on field codes. A search engine such as Google Scholar selects references matching text words, based on algorithms [5]. These algorithms change over time, often unexpectedly.…”