Editing Information science (section)

== Research sectors and applications ==
[[File:Getting_to_Philosophy_graph_of_Wikipedia_articles_by_Pine.png|thumb|This graph shows links between [[Wikipedia]] articles. Information science includes studying how topics relate to each other, and how readers relate concepts to each other.]]
The following areas are some of those that information science investigates and develops.

===Information access===
Information access is an area of research at the intersection of [[Informatics]], Information Science, [[Information Security]], [[Language Technology]], and [[Computer Science]]. The objectives of information access research are to automate the processing of large and unwieldy amounts of information and to simplify users' access to it. What about assigning privileges and restricting access to unauthorized users? The extent of access should be defined in the level of clearance granted for the information. Applicable technologies include [[information retrieval]], [[text mining]], [[text editing]], [[machine translation]], and [[text categorisation]]. In discussion, information access is often defined as concerning the insurance of free and closed or public access to information and is brought up in discussions on [[copyright]], [[patent law]], and [[public domain]]. Public libraries need resources to provide knowledge of information assurance.

===Information architecture===
{{Main|Information architecture}}

Information architecture (IA) is the art and science of organizing and labelling [[website]]s, [[intranet]]s, [[online communities]] and software to support usability.<ref>'What is IA?' Information Architecture Institute. [http://www.iainstitute.org/documents/learn/What_is_IA.pdf IAinstitute.org] {{Webarchive|url=https://web.archive.org/web/20070726070358/http://iainstitute.org/documents/learn/What_is_IA.pdf |date=2007-07-26 }}</ref> It is an emerging discipline and ''community of practice'' focused on bringing together principles of [[design]] and [[architecture]] to the ''digital landscape''.<ref>{{cite book | last1 = Morville | first1 = Peter | last2 = Rosenfeld | first2 = Louis | title = Information Architecture for the World Wide Web | publisher = O'Reilly Media, Inc | year = 2006 | isbn = 978-0-596-52734-1 | url-access = registration | url = https://archive.org/details/informationarchi00morv }}</ref> Typically it involves a [[Scientific modelling|model]] or [[concept]] of [[information]] which is used and applied to activities that require explicit details of complex [[information system]]s. These activities include [[library]] systems and [[database]] development.

===Information management===
{{Main|Information management}}

Information management (IM) is the collection and management of information from one or more sources and the distribution of that information to one or more audiences. This sometimes involves those who have a stake in, or a right to that information. Management means the organization of and control over the structure, processing and delivery of information. Throughout the 1970s this was largely limited to files, file maintenance, and the life cycle management of paper-based files, other media and records. With the proliferation of information technology starting in the 1970s, the job of information management took on a new light and also began to include the field of data maintenance.

===Information retrieval===
{{Main|Information retrieval}}

Information retrieval (IR) is the area of study concerned with searching for documents, for [[information]] within documents, and for [[metadata]] about documents, as well as that of searching [[structured storage]], [[relational database]]s, and the [[World Wide Web]]. Automated information retrieval systems are used to reduce what has been called "[[information overload]]". Many universities and [[public libraries]] use IR systems to provide access to books, journals and other documents. [[Web search engine]]s are the most visible [[Information retrieval applications|IR applications]].

An information retrieval process begins when a user enters a [[query string|query]] into the system. Queries are formal statements of [[information need]]s, for example search strings in web search engines. In information retrieval a query does not uniquely identify a single object in the collection. Instead, several objects may match the query, perhaps with different degrees of [[relevancy]].

An object is an entity that is represented by information in a [[database]]. User queries are matched against the database information. Depending on the [[Information retrieval applications|application]] the data objects may be, for example, text documents, images,<ref name=goodron2000>{{cite journal |first=Abby A. |last=Goodrum |title=Image Information Retrieval: An Overview of Current Research |journal=Informing Science |volume=3 |year=2000 |issue=2 }}</ref> audio,<ref name=Foote99>{{cite journal |first=Jonathan |last=Foote |title=An overview of audio information retrieval |journal=Multimedia Systems |year=1999 |doi=10.1007/s005300050106 |volume=7 |pages=2–10 |s2cid=2000641 }}</ref> [[mind maps]]<ref name=Beel2009>{{cite conference |first1=Jöran |last1=Beel |first2=Bela |last2=Gipp |first3=Jan-Olaf |last3=Stiller |title=Information Retrieval On Mind Maps – What Could It Be Good For? |url=http://www.sciplore.org/wp-content/papercite-data/pdf/beel09f.pdf|conference=Proceedings of the 5th International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom'09) |year=2009 |publisher=IEEE |place=Washington, DC }}</ref> or videos. Often the documents themselves are not kept or stored directly in the IR system, but are instead represented in the system by document surrogates or metadata.

Most IR systems compute a numeric score on how well each object in the database match the query, and rank the objects according to this value. The top ranking objects are then shown to the user. The process may then be iterated if the user wishes to refine the query.<ref name="Frakes1992">{{cite book |last=Frakes |first=William B. |title=Information Retrieval Data Structures & Algorithms |publisher=Prentice-Hall, Inc. |year=1992 |isbn=978-0-13-463837-9 |url=https://www.scribd.com/doc/13742235/Information-Retrieval-Data-Structures-Algorithms-William-B-Frakes |url-status=dead |archive-url=https://web.archive.org/web/20130928060217/http://www.scribd.com/doc/13742235/Information-Retrieval-Data-Structures-Algorithms-William-B-Frakes |archive-date=2013-09-28 }}</ref>

===Information seeking===
{{Main|Information seeking}}

Information seeking is the process or activity of attempting to obtain information in both human and technological contexts. Information seeking is related to, but different from, information retrieval (IR).

Much library and information science (LIS) research has focused on the information-seeking practices of practitioners within various fields of professional work. Studies have been carried out into the information-seeking behaviors of librarians,<ref>{{cite journal | last1 = Brown | first1 = C. M. | last2 = Ortega | first2 = L. | year = 2007 | title = Information seeking behaviour of physical science librarians: Does research inform practice | url = http://crl.acrl.org/content/66/3/231.short | journal = College & Research Libraries | volume = 66 | issue = 3 | pages = 231–247 | doi = 10.5860/crl.66.3.231 | doi-access = free | access-date = 2012-04-29 | archive-date = 2014-06-12 | archive-url = https://web.archive.org/web/20140612112147/http://crl.acrl.org/content/66/3/231.short | url-status = live }}</ref> academics,<ref>{{cite journal | last1 = Hemminger | first1 = B. M. | last2 = Lu | first2 = D. | last3 = Vaughan | first3 = K. T. L. | last4 = Adams | first4 = S. J. | year = 2007 | title = Information seeking behaviour of academic scientists | url = https://semanticscholar.org/paper/e9a09ecf7aa83d3735a1504c633f1ddb06366bac| journal = Journal of the American Society for Information Science and Technology | volume = 58 | issue = 14| pages = 2205–2225 | doi = 10.1002/asi.20686 | s2cid = 6142949 }}</ref> medical professionals,<ref>{{cite journal | last1 = Davies | first1 = K. | last2 = Harrison | first2 = J. | year = 2007 | title = The information-seeking behaviour of doctors: A review of the evidence | journal = Health Information & Libraries Journal | volume = 24 | issue = 2| pages = 78–94 | doi = 10.1111/j.1471-1842.2007.00713.x | pmid = 17584211 | doi-access =  }}</ref> engineers<ref>{{cite journal | last1 = Robinson | first1 = M. A. | year = 2010 | title = An empirical analysis of engineers' information behaviors | journal = Journal of the American Society for Information Science and Technology | volume = 61 | issue = 4| pages = 640–658 | doi = 10.1002/asi.21290 | s2cid = 15130260 }}</ref> and lawyers<ref>{{cite journal | last1 = Kuhlthau | first1 = C. C. | last2 = Tama | first2 = S. L. | year = 2001 | title = Information search process of lawyers: A call for 'just for me' information services | journal = Journal of Documentation | volume = 57 | issue = 1| pages = 25–43 | doi = 10.1108/EUM0000000007076 }}</ref> (among others). Much of this research has drawn on the work done by Leckie, Pettigrew (now Fisher) and Sylvain, who in 1996 conducted an extensive review of the LIS literature (as well as the literature of other academic fields) on professionals' information seeking. The authors proposed an analytic model of professionals' information seeking behaviour, intended to be generalizable across the professions, thus providing a platform for future research in the area. The model was intended to "prompt new insights... and give rise to more refined and applicable theories of information seeking" {{harv|Leckie|Pettigrew|Sylvain|1996|p=188}}. The model has been adapted by {{harvtxt|Wilkinson|2001}} who proposes a model of the information seeking of lawyers. Recent studies in this topic address the concept of information-gathering that "provides a broader perspective that adheres better to professionals' work-related reality and desired skills."<ref>{{cite journal |last1=Solomon |first1=Yosef |last2=Bronstein |first2=Jenny |date=18 February 2021 |title=The information-gathering practice of liberal professionals in a workplace setting: More than just seeking information |journal=Journal of Librarianship and Information Science |publisher=SAGE |volume=54 |issue=1 |pages=54–68 |issn=0961-0006 |doi=10.1177/0961000621992810 |s2cid = 233978764}}</ref> {{harv|Solomon|Bronstein|2021}}.

===Information society===
{{Main|Information society}}

An information society is a [[society]] where the creation, distribution, diffusion, uses, integration and manipulation of [[information]] is a significant economic, political, and cultural activity. The aim of an information society is to gain competitive advantage internationally, through using [[IT]] in a creative and productive way.  The [[knowledge economy]] is its economic counterpart, whereby wealth is created through the economic exploitation of understanding. People who have the means to partake in this form of society are sometimes called [[digital citizens]].

Basically, an information society is the means of getting information from one place to another {{harv|Wark|1997|p=22}}. As technology has become more advanced over time so too has the way we have adapted in sharing this information with each other.

Information society theory discusses the role of information and information technology in society, the question of which key concepts should be used for characterizing contemporary society, and how to define such concepts. It has become a specific branch of contemporary sociology.

=== Knowledge representation and reasoning ===
{{main|Knowledge representation and reasoning}}

Knowledge representation (KR) is an area of [[artificial intelligence]] research aimed at representing knowledge in symbols to facilitate [[inferencing]] from those [[knowledge]] elements, creating new elements of knowledge. The KR can be made to be independent of the underlying knowledge model or knowledge base system (KBS) such as a [[semantic network]].<ref name="martin" >[http://www.webkb.org/doc/papers/iccs02/iswc02.pdf "Knowledge representation in RDF/XML, KIF, Frame-CG and Formalized-English"] {{Webarchive|url=https://web.archive.org/web/20120326091831/http://www.webkb.org/doc/papers/iccs02/iswc02.pdf |date=2012-03-26 }}, Philippe Martin, Distributed System Technology Centre, QLD, Australia, July 15–19, 2002</ref>

Knowledge Representation (KR) research involves analysis of how to reason accurately and effectively and how best to use a set of symbols to represent a set of facts within a knowledge domain. A symbol vocabulary and a system of logic are combined to enable [[inference]]s about elements in the KR to create new KR sentences. Logic is used to supply formal [[semantics]] of how reasoning functions should be applied to the symbols in the KR system. Logic is also used to define how operators can process and reshape the knowledge. Examples of operators and operations include, negation, conjunction, adverbs, adjectives, quantifiers and modal operators. The logic is interpretation theory. These elements—symbols, operators, and interpretation theory—are what give sequences of symbols meaning within a KR.