Information Science
2005
Tefko Saracevic, PhD
School of Communication, Information and
Library Studies
Rutgers University
New Brunswick, New Jersey USA
http://www.scils.rutgers.edu/~tefko
© Tefko Saracevic
1
Information science:
a short definition
“the collection, classification,
storage, retrieval, and
dissemination of recorded knowledge
treated both as a pure and as an
applied science”
Merriam-Webster
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Organization of presentation
1.
2.
3.
4.
5.
6.
7.
8.
Big picture – problems, solutions, social place
Structure – main areas in research & practice
Technology – information retrieval – largest part
Information – representation; bibliometrics
People – users, use, seeking, context
Paradigm split – distancing of areas
Digital libraries – whose are they anyhow?
Conclusions – big questions for the future
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Part 1. The big picture
Problems addressed
α Bit of history: Vannevar Bush
(1945):
β Defined problem as “... the massive
task of making more accessible of a
bewildering store of knowledge.”
β Problem still with us & growing
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… solution
α Bush suggested a machine:
“Memex ... association of ideas ...
duplicate mental processes
artificially.”
α Technological fix to problem
α Still with us:
technological determinant
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At the base of information
science:
Problem
Trying to control content in
α Information explosion
β exponential growth of information
artifacts, if not of information itself
PLUS today
α Communication explosion
β exponential growth of means and ways by
which information is communicated,
transmitted, accesses, used
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technological solution, BUT …
applying technology
to solving
problems of
effective use of
information
BUT:
from a
HUMAN & SOCIAL
and not only
TECHNOLOGICAL
perspective
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or a symbolic model
People
Information
Technology
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Problems & solutions:
SOCIAL CONTEXT
α Professional practice AND scientific
inquiry related to:
Effective communication of knowledge
records - ‘literature’ - among humans in
the context of social, organizational, &
individual need for and use of
information
α Taking advantage of modern
information technology
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Elaboration
α Knowledge records =
β content-bearing structures
γ texts, sounds, images, multimedia, web ...
‘literature’ in given domains
α Communication =
β human-computer-literature interface
γ study of information science is the interface
between people & literatures
α Information need, seeking, and use =
β reason d'être
α Effectiveness =
β relevance, utility
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or as White & McCaine put it:
“modeling the
world of
publications
with a practical
goal of being
able to deliver
their content to
inquirers
[users] on
demand.”
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General characteristics
α Interdisciplinarity - relations
with a number of fields, some more
or less predominant
α Technological imperative - driving
force, as in many modern fields
α Information society - social
context and role in evolution shared with many fields
Table of content
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Part 2. Structure
Composition of the field
α As many fields, information science
has different areas of
concentration & specialization
α They change, evolve over time
β grow closer, grow apart
β ignore each other, less or more
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most importantly different areas…
α receive more or less in funding &
emphasis
β producing great imbalances in work &
progress
β attracting different audiences & fields
α this includes
β vastly different levels of support for
research and
β huge commercial investments &
applications
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How to view structure?
by decomposing areas & efforts in
research & practice emphasizing
Technology
or
Informatio
n
© Tefko Saracevic
People
or
Table of content
15
Part 3.
Technology
α Identified with information
retrieval (IR)
β by far biggest effort and investment
β international & global
β commercial interest large & growing
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Information Retrieval –
definition & objective
“ IR: ... intellectual aspects of
description of information, ... search,
... & systems, machines...”
Calvin Mooers, 1951
α How to provide users with relevant
information effectively?
For that objective:
1. How to organize information
intellectually?
2. How to specify the search &
interaction intellectually?
3. What techniques & systems to use
effectively?
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Streams in IR Res. & Dev.
1.
Information science:
Services, users, use;
β Human-computer interaction;
β Cognitive aspects
β
2.
Computer science:
β Algorithms, techniques
β Systems aspects
3.
Information industry:
Products, services, Web
β Market aspects
β
α Problem:
β relative isolation – discussed later
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Contemporary IR research
α Now mostly done within computer
science
β e.g Special Interest Group on IR,
Association for Computing Machinery
(SIGIR,ACM)
α Spread globally
β e.g. major IR research communities
emerged in China, Korea, Singapore
α Branched outside of information
science - “everybody does information retrieval”
β data mining, machine learning, natural
language processing, artificial
intelligence, computer graphics …
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Text REtrieval Conference (TREC)
α Major research, laboratory effort
α Started in 1992, now probably ending
β “support research within the IR community by
providing the infrastructure necessary for largescale evaluation”
α Methods
β provides large test beds, queries, relevance
judgments, comparative analyses
β essentially using Cranfield 1960’s methodology
β organized around tracks
γ various topics – changing over years
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TREC impact
α International – big impact on creating
research communities
α Annual conferences
β reports, exchange results, foster cooperation
α Results
β mostly in reports, available at
http://trec.nist.gov/pubs.html
β overviews provided as well
β but, only a fraction published in journals or
books
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TREC tracks 2004
103 groups from 21 countries
α Genomics with 4 sub
α HARD (High Accuracy
tracks
Retrieval from Documents)
α Novelty
(new, nonredundant
information)
α Question answering
α Robust (improving poorly
performing topics)
α Terabyte
(very large
collections)
α Web track
© Tefko Saracevic
α Previous tracks:
β
β
β
β
β
β
β
β
β
β
β
ad-hoc (1992-1999)
routing (92–97)
interactive (94-02)
filtering (95-02)
cross language (97-02)
speech (97-00)
Spanish (94-96)
video (00-01)
Chinese (96-97)
query (98-00)
and a few more run for
two years only
22
Broadening of IR –
ever changing, ever new areas added
α
α
α
α
α
α
α
α
α
α
α
α
Cross language IR (CLIR)
Natural language processing (NLP IR)
Music IR (MIR)
Image, video, multimedia retrieval
Spoken language retrieval
IR for bioinformatics and genomics
Summarization; text extraction
Question answering
Many human-computer interactions
XML IR
Web IR; Web search engines
IR in context – big area for major
search engines & newer research
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Commercial IR
α Search engines based on IR
α But added many elaborations &
significant innovations
β dealing with HUGE numbers of pages fast
β countering spamming & page rank games –
adversarial IR - combat of algorithms
β adding context for searching
α Spread & impact worldwide
β about 2000 engines in over 160 countries
β English was dominant, but not any more
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Commercial IR: brave new world
α Large investments & economic sector
β hope for big profits, as yet questionable
α Leading to proprietary, secret IR
β also aggressive hiring of best talent
β new commercial research centers in
different countries (e.g. MS in China)
α Academic research funding is changing
β brain drain from academe
α Commercial search engines facing many
challenges
β view from
© Tefko Saracevic
:Amit Singhal presentation
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IR successfully effected:
α Emergence & growth of the INFORMATION
INDUSTRY
α Evolution of IS as a PROFESSION &
SCIENCE
α Many APPLICATIONS in many fields
β including on the Web – search engines
α Improvements in HUMAN - COMPUTER
INTERACTION
α Evolution of INTEDISCIPLINARITY
IR has a long, proud history
© Tefko Saracevic
Table of content
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Part 4.
Information
α Several areas of investigation;
β as basic phenomenon – not much progress
γ measures as Shannon's not successful
γ concentrated on manifestations and effects
γ no recent progress in this basic research
β information representation
γ large area connected with IR, librarianship
γ metadata
β bibliometrics
γ structures of literature
Covered in lecture:
What is information?
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What is information?
Intuitively well understood, but
formally not well stated
β Several viewpoints, models emerged
α Shannon: source-channel-destination
β signals not content – not really
applicable, despite many tries
α Cognitive: changes in cognitive
structures
β content processing & effects
α Social: context, situation
β information seeking, tasks
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Cognitive – basic idea:
K(S) + ΔI = K(S + ΔS) (Brookes)
α Information when operating on a
knowledge structure produces an
effect whereby the knowledge
structure is changed
“Information is differences that
make a difference” (Bateson)
α Actually, it states the problem –
β “unoperational” in information systems
β involves mental events only
β constructivists rejected it
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Information manifestations
(Buckland)
α Information as a process
β what someone knows is changed when informed;
“the action of informing” (similar as Brookes)
γ refers to cognitive changes + process of
doing it
α Information as knowledge
β knowledge communicated about x
γ uncertainty removal a special case
γ refers to that which is being communicated intangible
α Information as a thing
© Tefko
β data, documents with quality of imparting
information - tangible
γ refers to potential information conveyed from
objects
Saracevic
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Information in information science:
Three senses
(from narrowest to broadest)
1. Information in terms of decision involving
little or no cognitive processing
β
signals, bits, straightforward data - e.g.. inf.
theory (Shanon), economics,
2. Information involving cognitive processing
& understanding
β
understanding, matching texts, Brookes
3. Information also as related to context,
situation, problem-at-hand
β
USERS, USE,TASK
For information science
(including information retrieval):
third, broadest interpretation necessary
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Bibliometrics
“… the quantitative treatment of the properties of
recorded discourse and behavior pertaining to it.”
Fairthorne
α Many quantitative studies & some laws
β Bradford’s law, Lotka’s law – regularities
γ quantity/yield distributions of journals, authors
α also related areas:
β Scientometrics
γ covering science in general, not just
publications
β Infometrics
γ all information objects
β Webmetrics or cybermetrics
γ using bibliometric techniques to study the web
© Tefko Saracevic
Table of content
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Part 5.
People
α Professional services
β in organization – moving toward knowledge
management, competitive intelligence
β in industry – vendors, aggregators, Internet,
α Research
β user & use studies
β interaction studies
β broadening to information seeking studies,
social context, collaboration
β relevance studies
β social informatics
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User & use studies
α Oldest area
β covers many topics, methods,
orientations
β many studies related to IR
γ e.g. searching, multitasking, browsing,
navigation
α Branching into Web use studies
β quantitative & qualitative studies
β emergence of webmetrics
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Interaction
α Traditional IR model concentrates
on matching but not on user side &
interaction
α Several interaction models
suggested
γ Ingwersen’s cognitive, Belkin’s episode,
Saracevic’s stratified model
β hard to get experiments & confirmation
α Considered key to providing
γ basis for better design
γ understanding of use of systems
α Web interactions: a major new area
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Information seeking
α Concentrates on broader context not only
IR or interaction, people as they move in
life & work
α Number of models provided
β e.g. Kuhlthau’s stages, Järvelin’s task based
α Includes studies of ‘life in the round,’
making sense, information encountering,
work life, information discovery
α Based on concept of social construction
of information
© Tefko Saracevic
Table of content
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Paradigm split in
technology - people
Part 6.
α Split from early 80’s to date into:
System-centered
β algorithms, TREC, search engines
β continue traditional IR model
Human-(user)-centered
β cognitive, situational, user studies
β interaction models, some started in
TREC
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Human vs. system
α Human (user) side:
β often highly critical, even one-sided
β mantra of implications for design
β but does not deliver concretely
α System side:
β mostly ignores user side & studies
β ‘tell us what to do & we will’
α Issue NOT
H or
S
approach
β even less H vs. S
β but how can H AND S work together
β major challenge for the future
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Calls vs support
α Many calls for user-centered or humancentered design, approaches & evaluation
α Number of works discussing it, but few
proposing concrete solutions
α But: most support for system work
β in the digital age support is for digital
α Recent attempt at combining two views:
Book: Ingerwersen, P. and Järvelin, K. (2005). The turn:
Integration of information seeking and retrieval in
context. Springer.
Table of content
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Part 7.
Digital libraries
α LARGE & growing area
α “Hot” area in R&D
β a number of large grants & projects in
the US, European Union, & other
countries
β but “DIGITAL” big & “libraries“ small
α “Hot” area in practice
β building digital collections, hybrid
libraries,
β many projects throughout the world
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Technical problems
α Substantial - larger & more complex
than anticipated:
β representing, storing
library objects
& retrieving of
γ particularly if originally designed to be
printed & then digitized
β operationally managing large collections issues of scale
β dealing with diverse & distributed
collections
γ interoperability
β assuring preservation & persistence
β incorporating rights management
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US: Digital Library Initiatives
α Consortia under National Science
Foundation funding research
β DLI 1: 1994-98, 3 agencies, $24M, 6 large
projects
β DLI 2: 1999-2006, 8 agencies, $60+M, 77
large & small projects in various categories
β joint international projects
β National Science, Mathematics, Engineering,
and Technology Education Digital Library
γ some 200 demonstration & development projects
α Funding pretty much over by 2005
β funding now in related areas
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EU: DELOS
α DELOS Network of Excellence on
Digital Libraries
β many projects throughout European Union
γ heavily technological
β
β
β
β
many meetings, workshops
to some degree resembles DLIs in the US
well funded, long range
unlike in the US support still going on
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Research issues
α understanding objects in DL
β representing in many formats
α
α
α
α
α
α
α
metadata, cataloging, indexing
conversion, digitization
organizing large collections
managing collections, scaling
preservation, archiving
interoperability, standardization
accessing, using, searching
β federated searching of distributed collections
α evaluation of digital libraries
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DL projects in practice
α Heavily oriented toward
institutions & their missions
β in libraries, but also others
γ museums, societies, government, commercial
γ come in many varieties
α Spread globally
β including digitization
α U California, Berkeley’s Libweb
“lists over 7300 pages from libraries in over
125 countries”
α Spending increasing significantly
β often a trade-off for other resources
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Agendas
α Most DL research agenda is set from top
down
β from funding agencies to projects
β imprint of the computer science community's
interest & vision
α Most DL practice agendas are set from
bottom up
β from institutions, incl. many libraries
β imprint of institutional missions, interests
vision
&
γ providing access to specialized materials and
collections from an institution (s) that are
otherwise not accessible
γ covering in an integral way a domain with a range
of sources
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Connection?
α DL research & DL
practice presently are
conducted
β mostly independently
of each other
β minimally informing
each other
β and having slight, or
no connection
α Parallel universes with
little connections &
interaction, at present
β not good for either
research or practice
© Tefko Saracevic
Table of content
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Part 8. Conclusions
IS contributions
α IS effected handling of information in
society
α Developed an organized body of
knowledge & professional competencies
α Applied interdisciplinarity
α IR reached a mature stage
β penetrated many fields & human activities
α Stressed HUMAN in human-computer
interaction
© Tefko Saracevic
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Challenges
α Adjust to the growing & changing social &
organizational role of inf. & related inf.
infrastructure
α Play a positive role in globalization of
information
α Respond to technological imperative in human
terms
α Respond to changes from inf. to
communication explosion - bringing own
experiences to resolutions, particularly to
the web
α Join competition with quality
α Join DIGITAL with LIBRARIES
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Juncture
α IS is at a critical juncture in its
evolution
α Many fields, groups ... moving into
information
β big competition
β entrance of powerful players
β fight for stakes
α To be a major player IS needs to progress
in its:
β
β
β
β
research & development
professional competencies
educational efforts
interdisciplinary relations
α Reexamination necessary
© Tefko Saracevic
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Thank you Miró!
© Tefko Saracevic
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© Tefko Saracevic
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Bibliography
Bates, M. J. (1999). Invisible Substrate of Information Science.
Journal of the American Society for Information Science,50, 10431050.
Bush, V. (1945). As We May Think. Atlantic Monthly, 176, (11), 101108. Available:
http://www.theatlantic.com/unbound/flashbks/computer/bushf.htm
Hjørland, B. (2000). Library and Information Science: Practice,
Theory, and Philosophical Basis. Information Processing &
Management, 36 (3), 501-531.
Pettigrew, K.E. & McKechnie, L.E.F. (2000). The use of theory in
information science research. Journal of the American Society for
Information Science and Technology, 52 (1), 62 - 73.
Saracevic, T. (1999). Information Science. Journal of the American
Society for Information Science, 50 (9) 1051-1063. Available:
http://www.scils.rutgers.edu/~tefko/JASIS1999.pdf
Saracevic, T. (2005). How were digital libraries evaluated?
Presentation at the course and conference Libraries in the Digital
Age (LIDA)30 May-3 June 2005, Dubrovnik, Croatia. Available:
http://www.scils.rutgers.edu/~tefko/DL_evaluation_LIDA.pdf
Webber, S. (2003) Information Science in 2003: A Critique. Journal of
Information Science, 29, (4), 311-330.
White, H. and Mc Cain, K. (1998). Visualizing a Discipline: An Author
Co-citation Analysis of Information Science 1972-1995. Journal of
the American Society for Information Science, 49 (4), 327-355.
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