Division of Informatics, University of Edinburgh

Centre for Intelligent Systems and their Applications
Institute for Adaptive and Neural Computation
Institute for Communicating and Collaborative Systems
Institute for Computing Systems Architecture
Institute of Perception, Action and Behaviour
Laboratory for Foundations of Computer Science

 

informatics

by Michael Fourman

Informatics Research Report EDI-INF-RR-0139

Division of Informatics July 2002
http://www.informatics.ed.ac.uk/

informatics

Michael Fourman

Informatics Research Report EDI-INF-RR-0139 DIVISION of INFORMATICS
Centre for Intelligent Systems and their Applications
Institute for Adaptive and Neural Computation
Institute for Communicating and Collaborative Systems
Institute for Computing Systems Architecture
Institute of Perception, Action and Behaviour
Laboratory for Foundations of Computer Science
July 2002

entry for ‘informatics’ to appear in International Encyclopedia of Information and Library Science (second edition) (0415259010) John Feather and Paul Sturges eds. Routledge 2002

Abstract :

This article is an extended entry in the Routledge International Enclopedia of Information and Library Science. It gives an account of the origins and meaning of the word ‘informatics’, and attempts to give some hint of the scientific depth, intellectual scope, and social importance of the subject, using examples relevant to the intended audience of this encyclopedia.

Keywords : informatics

Copyright © 2002 by Routledge

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The authors and the University of Edinburgh retain the right to reproduce and publish this paper for non-commercial purposes.

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informatics

Informatics  is  the  science  of  information.   It   studies   the   representation,   processing,   and   communication   of information   in   natural   and   artificial   systems. Since   computers,   individuals    and    organizations    all  process information, informatics has computational, cognitive and social aspects.

Used  as  a  compound, in  conjunction   with   the  name   of  a  discipline,   as  in  medical   informatics,   bio-informatics, etc.,  it  denotes the  specialization of  informatics to  the  management   and   processing   of   data,   information   and knowledge in the named discipline.

Terminology

The  French   term   informatique,   together   with   various   translations   —   informatics  (English),   informatik   (German), and informatica (Italian, Spanish)  —  was  coined  by  Dreyfus,  in  March  1962  (Dreyfus  1962),  referring  to  the application  of  computers  to  store  and  process  information  (see  also  Bauer  1996).   The  morphology,  information  + –ics,  uses  ‘the  accepted  form  for  names  of  sciences, as  conics, linguistics, optics, or   matters   of   practice,   as economics, politics, tactics’ (Oxford English Dictionary 1989); informatics encompasses both science and practice. Phonologically,  informatics  combines elements   from   both   ‘information’   and   ‘automatic’,   which   strengthens  its semantic appeal.  This new  term was  adopted across Western Europe, and, except in  English,  developed  a meaning roughly translated by  the   English   ‘computer   science’,   or   ‘computing   science’.   Mikhailov   et   al.   advocated   the Russian term ‘informatika’ (1966), and the  English  ‘informatics’  (1967),  as  names  for  the  ‘theory  of  scientific information’, and argued for a broader meaning, including study  of  the  use  of  information technology in various communities (e.g. scientific) and of the interaction of technology and human organizational structures.

‘Informatics is  the  discipline of  science which  investigates the   structure   and   properties   (not specific content) of  scientific information, as  well  as  the  regularities of   scientific   information activity, its theory, history, methodology and organization.’

(Mikhailov et al. 1967)

Usage  has  since  modified  this  definition  in  three  ways.  First,  the  restriction  to  scientific  information  is  removed, as in business informatics or legal informatics. Second,  since  most  information  is  now  digitally  stored  (Lesk,  1997; Lyman  et  al.  2000),  computation  is  now  central   to   informatics:   Gorn   (1983)   defines  informatics   as   computer science plus information science. Third, the processing and communication of  information are  added  as  objects  of investigation, since they have been recognized as fundamental to any scientific account of information.

In  the  English-speaking world  the  term  informatics   was   first   widely   used   in   the compound,  ‘medical informatics’, taken to include ‘the cognitive,  information  processing,  and  communication  tasks  of  medical  practice, education,  and  research,  including  information  science  and  the  technology to  support these  tasks’ (Greenes and Shortliffe 1990). Many such compounds are now in use.

A June 2002 web  search (Google 2002)  found  ‘informatics’ and  various compounds, occurring more  or  less frequently: the numbers of documents returned for  each  term  are  given  in  parentheses: informatics  (1,100,000), bioinformatics (691,000), medical informatics (151,000), health informatics (52,800), museum informatics (19,500),nursing informatics (15,600), geoinformatics (11,100), neuroinformatics (9,180), social informatics (6,840), business informatics (6,610), dental informatics (2,850), molecular informatics (2,630), environmental informatics  (2,580),  legal informatics (1,640),   chemical  informatics   (1,230),   mobile  informatics   (492),  protein informatics (408),  and  library informatics (303).  Some  informatics specializations are  named  in   other   ways:   for example,  what might  be   called  science  informatics (1,710)   is   more   usually   called   e-science   (17,700);  bioinformatics is often called computational biology (347,000).

Each of these areas  studies  representations  and  uses  of  information, which  may  be  peculiar to  each  field of application,  but  draw  on  common  social, logical and  computational foundations. They  all   involve   the   use   of computing and information technologies to store, process and communicate information. They all also address the interaction  of  technology  with  the  production  and   use   of   information   by   individuals   and   organizations;   they develop  software,  systems  and  services   that   aim   to   help   people   interact   with   information,   efficiently  and effectively.

The scope of Informatics

What these areas have in  common  is  informatics:  the  focus  on  information  and  how  it  is  represented  in,  processed by,  and   communicated   between   a  variety   of systems.     Representations   include   paper,   analogue,  and  digital  records of text, sounds and images, as well as, for instance, the information represented in a gene,  and  the  memories  of  an individual  or   an   organization.  Processing  includes  human   reasoning,    digital    computation,    and   organizational processes. Communication  includes  human communication  and  the  human-computer interface   –   with   speech   and gesture, with  text   and  diagram,   as  well   as  computer   communications   and  networking,   which   may  use  radio, optical or electrical signals.

Informatics  studies  the  interaction  of   information   with   individuals   and   organizations,   as   well   as   the fundamentals  of  computation  and  computability,  and  the  hardware  and  software  technologies   used  to  store,   process and communicate  digitised information.  It  includes  the  study  of  communication  as  a   process   that   links   people together, to affect the behaviour of individuals and organizations.

Informatics as a Science

Science progresses by defining, developing, criticizing and refining new concepts, in  order  to  account for  observed phenomena. Informatics is developing its own fundamental concepts of communication,  knowledge,  data, secrecy, interaction and  information, relating them  to  such  phenomena as  computation, thought,   and   language,   and  applying them to develop tools for the management of information resources.

Informatics  has  many  aspects.  It  encompasses,  and   builds   on,   a   number   of   existing  academic  disciplines: primarily Artificial Intelligence, Cognitive Science and  Computer Science.   Each   takes   part   of   Informatics   as   its natural domain: in broad terms, Cognitive Science concerns the  study  of  natural  information  processing  systems; Computer  Science  concerns  the  analysis  of   computation,   and   the   design   of   computing   systems;   Artificial Intelligence plays a connecting role, producing systems designed to emulate those found  in  nature.  Informatics  also informs,  and  is  informed  by,  other  disciplines,  such  as   Mathematics,   Electronics, Biology, Linguistics,  Psychology, and  Sociology. Thus   Informatics   provides   a   link   between disciplines   with   their   own   methodologies   and perspectives, bringing  together  a   common   scientific  paradigm,  common   engineering   methods  and   a  pervasive stimulus from both technological development and practical application.

Informatics builds  on  a  long  tradition  of   work  in  logic,   which  provides  an  analysis   of  meaning,   proof   and   truth. It draws on probability and statistics to  relate  data  and  information,  and  on  the  more  recent  tradition  of  computer science  for  abstract  models  of   computation,   and  fundamental   notions  of   computability  (What   can   be computed?) and  complexity (How  do  the  space  and  time  requirements of  a  computation  scale  as  we   consider   problems of different sizes?). Combining these traditions enriches them all, since they share a common interest in information.

The  science of  information  provides  a  new  paradigm  of  scientific  analysis,  which  concentrates  on  the  processing and   communication  information,   rather   than   focussing   on   the    electrical,    optical,    mechanical    or  chemical interactions  that  embody  this  activity.  Focussing   on   information   provides   novel   accounts   of   long-standing phenomena, even in Physics (Frieden 1998). Informatics provides accounts of the representation, processing,  and communication   of   information,   the   conceptual   basis   for   applying   computing   and   information   technologies    to develop new tools  for  the  management  of  information  in  diverse  areas,  and also  the basis  for beginning  to  unravel the workings of the mind.

We illustrate  the  scope  and  nature  of  informatics  with  a  brief  account  of  one  area  in  which  informatics is contributing   to   library   and   information   science.   With   essentially  all   information    becoming    available    online, libraries will  focus  increasingly on  selection, searching, and  quality assessment. Informatics’  contribution  to  this enterprise is to provide and apply appropriate techniques for the  representation,  processing  and  communication  of information. We give an account of some of these techniques, with a focus on textual information.

Representation

‘The Web was designed as an information space, with the goal that it should be useful not only for human-human communication, but   also   that   machines   would   be  able to  participate   and   help. One of the major obstacles to this has  been  the  fact  that  most  information  on  the  Web  is  designed  for human consumption. […]  The  Semantic Web  approach […]  develops languages for  expressing information in a machine processable form.’

(Tim Berners-Lee 1998)

Data  on  the  World  Wide  Web  makes  digital representations familiar to  us  all.  Texts,  images  and  sounds   are represented   by  digital   encodings,   as  patterns   of  bits.   Standards,   such   as  ASCII,   Unicode,   JPEG,   TIFF,   WAV and

MP3,  allow  for  the  encoding,  storage,   exchange,   and   decoding   of   multimedia   information.   These   representations may  be  indexed and  retrieved  as  individual  files,  but  they  are,  to  varying  degrees,  opaque   to   software   agents searching for information.

Text  files  are  stored  as  unstructured  sequences  of  characters.  Dividing  a   text   into   words,   sentences   and paragraphs is mostly straightforward. Finding keywords in  a  text  file  is  straightforward, and  some  search engines incorporate shallow linguistic knowledge that extends keyword search. For example, ‘stemming’  determines  the morphological  root of  a  given word form,   thus   relating  singular  and plural  forms   of  a  noun,   and  different moods and  tenses  of  a  verb.   Searching   for  swan,  using  stemming   finds  swans,  and  searching  for  goose  should  find geese (see free text searching) .

We  can  also  search  for  content in  other  media.  Finding keywords in  recorded   speech   is  much   harder than searching  a  text file: more expensive in   computing  resources,  and   less   accurate.  Finding   images   with   specified content, just by examining the image automatically, is even harder (see image retrieval).

Extracting information from  texts  requires   deeper   linguistic   analysis.   For example, searching   news   reports   to glean  information  about  company  takeovers   –  who  is  being  taken  over,  by  whom,  what  price  is  being  paid,  for  what requires a grammatical analysis of individual  sentence  structures.  More  complex  information  – such  as  finding arguments that support  a  particular  decision  –  demands more global analyses  of  meaning.  Text  files  form  a small fraction of  society’s data  storage (Lyman  and  Varian  2000).  However, texts  are  rich  in  information that  is not represented elsewhere, so it is important to make this information accessible. If texts are stored  in  ways  that  make grammatical and  rhetorical structures transparent, then  it  becomes easier   for   automated   tools   to   access such information.

Metadata

The simplest way of making searching  easier  is  to  attach  an  electronic  ‘catalogue  card’  to  each  document.  Such data about data is called ‘metadata’. For example,  the  Dublin  Core  is  a  metadata  standard  which  specifies  a  set of required and  permitted elements for  such  a  catalogue card.  The  Resource Description Format  (RDF)  is  a general standard for such metadata.

Metadata  allows  software  agents  to  find,  retrieve,  and  process  data.  Just  as  books  in  a  library   are   made accessible by the catalogue, so information on the web is made accessible by metadata.

The Semantic Web is  a  project  that  aims  to  provide  a  common  framework  for  such  efforts,  by  having  data  on the web defined and linked in such  a  way  that  it  can  be  used  by  machines not  just  for  display  purposes,  but for automation,  integration  and  reuse  of  data  across  various  applications,  so  that  tomorrow’s  programs  can   share and process data even when these programs have been designed totally independently.

Structured data

Digital   documents   allow,   in   principle,   much   richer   automated   processing   –   content   selection,    information extraction,  price  comparisons,  or  document  clustering.  To  facilitate  this,  data  is  structured;  documents   are   given internal  structure.  There  are  many  different  formats  for  structured  data,  some  simple  in  their  description,  others complex and  rich.  Many  communities (biologists, engineers,   geologists,   businesses)   are   designing   new   formats   to allow them to put machine-understandable data on the  Web.  This  will  allow  data  to  be  shared  and  processed  by automated tools as well as by people.

Relational Data

Relational databases represent information by representing relations  between  entities  (such  as  the  relation  between book  and  author, or  the  relation between book  and  publisher).   Rather   than   exchange  whole   databases,   query languages, such as SQL, allow users to retrieve, from the database, information about specified  entities.  Relational databases  are  appropriate  for  representing  uniformly  structured  data,  where  all  entities  of  a  given  type  can  be represented by  specifying a  given  collection of  relations   (every   part   has  a  price,   every   employee   has  a manager). But they are  ill  adapted  to  the  open-ended  nature  of  information  on  the  web,  where  we  may  find  that  one  manager also plays in a band, and so have to represent the fact that she plays the saxophone.

Markup

One common form  of  structuring is  markup. Markup  originated as  a  means  of  structuring text;  before  the computerization  of  the  printing  industry,  markup  was  annotation  written  by  a  copy   editor   on  a  manuscript,  to indicate structure (chapter, heading, paragraph…) and style (italic, bold, etc.). Markup now refers  to sequences  of characters, known  as  tags,  inserted in  a  text   or  word   processing   file.   The  original   use  of  markup   was  to indicate how  the  file  should   look  when  printed   or  displayed.   Markup   is  now  often  used  to  describe   a  document’s       logical structure,  or  as  a  format  for  describing  an  abstract  logical   structure,   so-called   ‘semi-structured  data’   –  a replacement for the representation of structured data by traditional relational databases.

Standard frameworks for markup, such as SGML (Standard Generalized Markup Language) and  XML  (eXtensible Markup Language),  have been  adopted by  many  metadata initiatives, including   the   semantic   web,   where   XML markup is used to structure the metadata  attached  to  a  document.  An  example  of  SGML  markup  is  the  HyperText Markup Language (HTML), lingua franca of the World Wide Web.

A  general markup language,  such   as  XML,   can  be  applied  to  encode  content  and  structure  for  applications   that go far beyond the original purposes of markup for typesetting and display.  Markup can be used to tag entities in a document  (addresses,  prices,  or  names),  to  tag  logical  roles  (author,  publisher),  to   tag   logical   connections,   for example linking a price to an entity, or  to  tag  phrases and  parts  of  speech, in  order  to  indicate a  text’s  detailed grammatical structure. Applications of XML  are  found  everywhere:  in  bioinformatics  and  linguistics,  in business- to-business applications, in cataloguingand indexing, and in scholarly annotation of ancient texts.

Markup languages define  the  scope  of  what  can  and  cannot  be  expressed   in   markup .   For   example, XML provides controlled flexibility, and allows us to represent semi-structured  data that does not fit well in the relational mould.  XML  tags  come  in  pairs,  <author>…</author>,  which  act  as  “named  brackets”.   These  brackets  must be properly nested, so an XML  document  has  a  hierarchical  structure  in  which  each  layer  of  the  hierarchy consists of text,  interspersed   with  elements   from  the  layer  below  tagged  with  names  (such  as  “author”).  This  allows  a  mixture of structure and free text .

Most  applications  require  further  restrictions.  For  example,  HTML  is  defined  by  a  document  type  definition (DTD)  that  specifies  which  elements  may  occur  in  an   HTML   document   (headings,   paragraphs,   lists…),   and structural rules (for example, list elements occur within lists) that  an  HTML  document  must  follow.  A DTD can specify as many constraints on the  structure  as  are  needed  for  a  particular  application,  or  as  few.  So  an author element might require a surname, allow any number of forenames, and permit nothing else.

From a scientific perspective,  XML  and  the  structures  it  allows  us  to  express  represent  just  one  of  many possibilities for structuring  data.  The science underlying  XML provides  an  understanding  of  the  ways  in  which  data may be structured.  It  provides  query  languages  and  algorithms  for  retrieving  data  in  response  to  a query.  It  provides the  conceptual  framework  for  understanding  how  structure  may  be   specified,   for   example   by   a   DTD, and algorithms for checking that a document conforms to  a  specified form.  It  provides  the  basis  for  assurances  of the integrity and provenance of data, and so on.

Processing and Communication

Processing is the transformation of data from one form to another. Information is data interpreted,  organized  and structured. For  example, the  English documents on  the  web  form  a  large  dataset; when  we  use  a  search  engine  to count  the  numbers of  documents containing a  given  search   term   it  finds   on  the  web,   we  extract   information from this  data.  The  ability  to  collect, aggregate, and  organize data  allows  us  to   create   and   represent   information. Knowledge is  information that has  been analysed   so   that   inter-relationships   are   identified,   formalized,  and represented. Thus  processing can  extract information from  data,  and  transform  information  into   knowledge.   These results must then be communicated effectively to the user.

Natural Language Processing

Today  (2002), machines are  widely  used  for   document  retrieval.   Future   software   agents   will   use   Natural Language  Processing  (NLP)  tools  to   extract   relevant   information   from   documents   in   response   to   user   queries, create summaries tailored to  the   user’s   needs,   and   collate,  assemble  and   present   information  derived   from   a multitude of sources.

Human readers see structure  in  texts:  words,  sentences,  paragraphs,  documents….  They  attribute meanings to documents, and  structure   these   meanings   –   as   facts,   arguments   and   conclusions,   and   so   on.  Representing, processing and  communicating structures and  meanings, in  ways  that  make  these  easily  accessible to   machine processing, so that users can easily access information relevant to their needs, are key issues for informatics.

Automated text -processing  tools  can  tag  parts   of   speech,   and   annotate   grammatical   links:   the   connection between verb, subject, and object; the connection between a pronoun and the phrase it refers  to.  Deeper  linguistic processing can  disambiguate word  senses. Such   tools   are   components   of   natural   language   understanding.   They convert texts into  machine-accessible  sources of  information,  and  provide  the  basis  for  a  variety  of  information processing applications.

For example, current document retrieval systems ,  using  keyword  search,  allow  users  to  find  documents  that are relevant to their needs, but most leave it to the user to extract the useful information from those documents. Users, however, are often looking for answers, not documents.  Information extraction tools,  based on  natural  language understanding,    find    and   extract    information    from    texts.    Such    tools,   already   used,   by   intelligence   analysts  and others,  to  sift through large  amounts of  textual information,  will   become  commonplace.   Document  clustering   is another  application  that   draw s   on   natural  language  technology.   By   examining   patterns   of   word occurrences, together with syntactic and semantic  structures,  it  is  possible  to  cluster  documents  by  topic, or  to  search  for documents similar to a given example.

Automated natural language  generation  is  also being  applied,  to  new  forms  of  information  delivery.  Machine- generated text can be used to present information tailored to  the  user  (Oberlander  et  al.  1998).  Such tools will be applied to present information  derived  from  database  queries,  information  extraction,  and data mining.  Tools  for document clustering  will be  linked to  natural language generation  to   provide   automatically   generated  summaries drawing on a variety of sources.

Natural language processing is  one  example  demonstrating  the  way  informatics  relates  to  longer-established disciplines.  It  relies  on  computer   science   for   underlying   software   and   hardware   technologies,   and   for   algorithms that make this processing feasible. It draws on  logic  and  linguistics  for  appropriate  representations  of  linguistic  and semantic structures, on machine learning techniques from artificial intelligence for tools  that  extract  from  large  text corpora information  on  the  words  and  concepts  relevant  for  a  particular domain,  and on  cognitive  science and psychology for an understanding of how people process and react to information.

These disciplines  are  drawn   together,  in   informatics,  by   the   common   purpose   of   understanding  how   language can communicate  information  between  human  users  and a formal  representation  stored  in  a machine.  This marriage of  computational and  theoretical linguistics   with   cognitive   psychology   and   neuroscience   has   generated   new   tools, and also thrown new light on human communication.

It  is  clear  that  information, and hence informatics, must  play  a  pivotal  role  in  any  analysis   of   human communication. Informatics  is  also  transforming other  areas  of  science: it  provides  a  new  paradigm  for  analysing complex  systems,  as  compositions   of   simpler   subsystems   that   process   and   communicate   information.  Long- standing challenges to scientific analysis, are being transformed by the new paradigm .

For example,  in  biology ,  informatics  provides  not  just  tools   for  data-processing  and  knowledge   discovery,   but also a conceptual framework for studying the  information  stored  and  communicated by  genes,  and  processed  by biochemical cycles that ‘run the genetic program’ to produce structure and form.

In  cognitive science it  provides the  conceptual  tools  needed  to  develop  models   of the   connection   between cognition  and the  observed  structure  and  function  of  the  billions  of  neurons  that  make  up  the  brain.  It  also   provides the technologies that  allow  us  to  observe  and  analyse the  structure  and  operation  of  the  living  brain  in  ever  greater detail, and to test  our  models by  simulation  of  very  much  simplified  subsystems,  which,  despite  their  relative simplicity, are complex beyond analytical analysis.

Looking forward

The technologies underlying the  digital storage, processing and  communication of  information are improving relentlessly. Since 1965 these improvements  have  followed  ‘Moore’s  Law’:  for  a given  price,  both  processor  speeds and  memory  capacity  double  every  18—24  months  (Moore  1965).   Communication   bandwidth   follows   a  similar pattern of  growth,  but  doubles  in  12  months  or  less  (Poggio  2000).  These  rates  of  exponential  growth  are  predicted to  continue, so  processing  speed  and  memory  capacity  will  increase  by  a  factor  of   100,   and   communication bandwidth by a factor of 1000 or more, every ten years.

The  combination of  digitisation  and global  connectivity  makes  data available  in  unprecedented   volume.  It   is estimated   that  humanity   creates   more  than  an  *exabyte   of  data  each  year  (Lyman  and  Varian,   2000).    Nevertheless, it will  soon  be  technologically possible for  an  average person  to  access  virtually all  recorded information.  The availability  of  cheap  processing  will  make  it  increasingly   feasible   to   restructure   data   into   knowledge   on demand. New  technologies are  being  developed to  automatically organize this   material   into   forms   that   can   help   people quickly and  accurately  satisfy  their  information  needs,  realizing,  and  surpassing,  Vannevar  Bush’s  prescient  vision of the ‘Memex’ – ‘a device in which an individual stores all  his  books,  records,  and  communications,  and  which  is mechanized so  that  it  may  be  consulted with  exceeding speed  and  flexibility’ (Bush  1945).  These  technologies  are indeed ‘creating a new relationship between thinking man and the sum of our knowledge’.

By 2025, if Moore’s law  continues  to  apply,  we  will  have,  in  our  pockets  and  on  our  desktops,  computers  that each  have  the  raw  computing power  of  the  human  brain,  computers  linked  to  each  other  by  a telepathic communication network. We  currently have  little  idea  of  how  we  might  structure   and   program   such   devices   to achieve  what  we  humans  find  straightforward,  but  already  today’s   machines   extend   our  capabilities  by performing tasks  we  find  impossible. We  can  be  sure  that  technological changes will  continue to  revolutionize the  ways  we manage, share, and analyse data, and will provide new ways of transforming data into information and knowledge.

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FID News Bull. 17(2), pp. 70 —4.

Moore, G.E. (1965) Cramming more components onto integrated circuits. Electronics, 38(8) pp. 114—7. http://www.intel.com/research/silicon/moorespaper.pdf

Oberlander, J. O’Donnell, M. Knott A. and Mellish, C. (1998). Conversation in the museum: experiments in dynamic hypermedia with the intelligent labeling explorer. New Review of Hypermedia and Multimedia, 4, pp. 11—32.

Oxford English Dictionary (1989) second edition. Oxford University Press.

Poggio, A. (2000) Information and Products, in Englebart’s Colloquium, the unfinished revolution. Stanford http://www.bootstrap.org/colloquium/session_10/session_10_poggio.jsp

Further Reading

Association for Computational Linguistics (ACL) http://www.aclweb.org/

Cole, Ron et al. (eds), (1998) Survey of the State of the Art in Human Language Technology. Studies in Natural Language Processing, Cambridge University Press; ISBN: 0521592771. http://cslu.cse.ogi.edu/HLTsurvey

Cover, Robin (Ed). (2002) XML Cover Pages, http://www.oasis-open.org/cover/sgml-xml.html

Graves, J.R. and Corcoran, S. (1989) ‘The Study of Nursing Informatics’, Image: Journal of Nursing Scholarship, 21, pp.

227—31 http://www.nih.gov/ninr/research/vol4/Overview.html Martin, W.J. (1988) The Information Society, London: ASLIB.

Musen, Mark A. (1999) ‘Stanford Medical Informatics: Uncommon research, common goals’. MD Computing, January/February 1999, pp.47— 50. http://camis.stan ford.edu/MDComputing.pdf

Shortliffe, E.H. Perreault, L.E. Wiederhold, G. and Fagan, L.M. (1990) Medical Informatics: Computer Applications in Health Care, Addison-Wesley.

Social  Informatics http://www.slis.indiana.edu/si/concepts.html

Text Retrieval Conference (TREC) http://trec.nist.gov/

See also: artificial intelligence; communication; communication and IT;  computer  science;  data  modelling; database; database management; human-computer interaction; hypertext; indexing;  information retrieval; information  science;  intelligent  agents;  knowledge  management;  knowledge -based   systems;  machine translation;   mark-up   languages;   metadata;   neural   network;   relational   database;   information   systems; search engines; software; string indexing; World Wide   Web.

(4107 words)

MICHAEL  P. FOURMAN