Information science (IS) is a multidisciplinary field concerned with “facilitating the effective communication of desired information between human generator and human user” (Belkin 1978, 58). IS became established as an academic discipline with the creation of the American Society for Information Science in 1937 (now abbreviated ASIS&T) and the UK Institute of Information Scientists in 1958.
Previously, work on scientific and technological information had been called documentation, and had its roots in French and US universities at the beginning of the twentieth century. The foundation for quantitative analyses of scholarly communication (scientometrics, bibliometrics) was laid in studies by pioneers like A. J. Lotka (on scientific productivity and publication ratios, 1926), G. K. Zippf (on rank and frequency of terms in large text corpora, 1932 – later applied to automatic indexing in information retrieval research), and S. C. Bradford (on the distribution of academic articles about a scientific topic across journals, 1934). These pioneering efforts were accelerated by the digital computer. From the 1950s, former laboratory scientists began, as information scientists, to develop, manage, and evaluate systems for the retrieval and use of technical and scientific information, as well as to analyze the impact of information and information technologies (IT) on scientific communication. The invention by the chemist E. Garfield of global citation databases for the sciences is typical of this period, as are the works by Derek De Solla Price and Robert K. Merton on the communication of scientific knowledge.
Throughout its history, IS has witnessed a struggle between technological and humancentered perspectives on communication. For almost two decades (roughly 1960 –1980), an identity crisis of sorts followed from the awareness that theories of IS are not founded on mathematical or natural laws. Several attempts were made to construct a foundation, drawing on, for example, computer science or communication theory. In the former approach, IS became IT-driven, designing and maintaining abstract or full-text databases or, more recently, developing web retrieval systems. However, since the end of the 1970s, IS increasingly has been regarded as a social science discipline affiliated with communication research, as evidenced by a growth in user-oriented approaches attempting to have IT “fit humans.”
This shift of focus coincided with a new generation of IS researchers who originated not from the sciences, but from IS departments established by the pioneers. Today, the communication approach is predominant especially in Anglo American and Scandinavian university IS departments and schools. Since the 1970s, research and education in IS has also been concerned with librarianship, information studies, and library and information science (LIS). In the US, a recent phenomenon is I-schools, i.e., university departments merging communication, IT, and LIS into information schools.
There are several central research areas in IS. Information retrieval (IR) is the mainstream R&D area of IS. It addresses algorithmic retrieval and evaluation experiments regarding performance in retrieving document contents, including unstructured texts, images, sound, speech, music, and multimedia. It also undertakes search engine development, interface usability, and field investigations of interaction between information searchers and knowledge sources in context. Knowledge organization concerns epistemological (macro-level) and applied (micro-level) ways of representing documents by (algorithmic and/or human) indexing. It also works with classification methods and systems, and thesaurus/ontology architectures, across genres, media, and scientific as well as other social domains of application. Bibliometrics and informetrics covers quantitative studies of both scientific and everyday communication, and of technological innovation (e.g., patents), including citation analyses and research evaluation (scientometrics) and analyses of such Internet structures as links (webometrics). Information seeking behavior studies the search for and use of (informal) information sources and communication channels. Library research examines the role and impact of public, university, or digital libraries, open access journals and repositories, and other knowledge centers in local communities and wider society, including information literacy and studies of selected user groups. Knowledge management addresses economic and organizational techniques for managing information resources and (tacit) knowledge sources and sharing in institutional contexts.
Information is one of the key concepts of IS and is commonly understood as an intentional transformation of certain actors’ cognitions, presented as a message in the form of signs that transforms the recipients’ state of knowledge. Information, thus, becomes an increment of knowledge, always situated in a context, as the message is perceived and interpreted by particular recipients. In a semantic sense, a message can provide the same information to many recipients. In a pragmatic sense, information is the result of individual interpretations in context.
Information need development refers to both individual and collective information needs. In order to solve a perceived task (at work or in daily life), actor(s) may require information. If they do not acquire information, they will encounter cognitive-emotional problems, developing a state of uncertainty that is associated with an information gap or need. When actors have a loose understanding of their tasks or problem situations, or a low awareness of their information needs, they tend not to take full advantage of IR systems, e.g., by communicating very few or broad search keys to the systems or by replacing systems entirely by human knowledge sources.
Relevance is intuitively familiar, but difficult to measure during IR experiments. One relevance typology (Borlund 2003) distinguishes subjective, intangible relevance from objective, tangible relevance. Objective relevance is defined either through an algorithmic calculation of the best fit within the IR system for a searcher’s request, or by so-called socio-cognitive relevance. Over time, collective assessments are made of the value of an item, e.g., via citations or links to recognized work. Subjective relevance is established through assessments of topicality, pertinence, and situational applicability. Topicality considers the correlation between the contents of available documents and what the searcher asked for (their common “about-ness”); pertinence concerns the relationship between document contents and the searcher’s intrinsic information need; and situational relevance captures the association between the meaning of the document contents and the perceived underlying task of the actor.
- Belkin, N. J. (1978). Information concepts for information science. Journal of Documentation, 34, 55 – 85.
- Borlund, P. (2003). The concept of relevance in IR. Journal of the American Society for Information Science and Technology, 54(10), 913 –925.
- Ingwersen, P. (1992). Information retrieval interaction. London: Taylor Graham, pp. 1–14.
- Ingwersen, P., & Järvelin, K. (2005). The turn: Integration of information seeking and retrieval in context. Heidelberg: Springer.
- Vickery, B. C., & Vickery, A. (1987). Information science in theory and practice. London: Butterworth.
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