1. Information Science: Where does it come from and where is it going?
Tefko Saracevic, PhD
School of Communication, Information and Library Studies
Rutgers University
New Brunswick, New Jersey USA
Gutenberg
© Tefko Saracevic
© Tefko Saracevic

2. 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
© Tefko Saracevic
© Tefko Saracevic

3. Organization of presentation
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
Relations – librarianship, computer science
Digital libraries – whose are they anyhow?
Conclusions – big questions for the future
© Tefko Saracevic

4. Part 1. The big picture Problems addressed
Bit of history: Vannevar Bush (1945):
Defined problem as “... the massive task of making more accessible of a bewildering store of knowledge.”
Problem still with us & growing
© Tefko Saracevic

5. … solution
Bush suggested a machine: “Memex ... association of ideas ... duplicate mental processes artificially.”
Technological fix to problem
Still with us: technological determinant
© Tefko Saracevic

6. At the base of information science: Problem
Trying to control content in
Information explosion
exponential growth of information artifacts, if not of information itself
PLUS today
Communication explosion
exponential growth of means and ways by which information is communicated, transmitted, accesses, used
© Tefko Saracevic

7. technological solution, BUT …
applying technology to solving problems of effective use of information
BUT:
from a HUMAN & SOCIAL
and not only TECHNOLOGICAL perspective
© Tefko Saracevic

8. or a symbolic model
Information
Technology
People
© Tefko Saracevic

9. Problems & solutions: SOCIAL CONTEXT
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
Taking advantage of modern information technology
Think about the White and McCain formulation: does not sound glamorous, but that’s what we try to do.
With the help of technology of course, sons, daughteters & grand cousins of Memex. Sometimes we think that we are about techniology alone. But we are not. Also but: as in many other applications: technology is the tail that wags the dog.
© Tefko Saracevic
© Tefko Saracevic

10. or as White & McCaine (1998) put it:
“modeling the world of publications with a practical goal of being able to deliver their content to inquirers [users] on demand.”
© Tefko Saracevic

11. General characteristics
Interdisciplinarity - relations with a number of fields, some more or less predominant
Technological imperative - driving force, as in many modern fields
Information society - social context and role in evolution - shared with many fields
Table of content
© Tefko Saracevic

12. Part 2. Structure Composition of the field
As many fields, information science has different areas of concentration & specialization
They change, evolve over time
grow closer, grow apart
ignore each other, less or more
sometimes fight
© Tefko Saracevic

13. most importantly different areas…
receive more or less in funding & emphasis
producing great imbalances in work & progress
attracting different audiences & fields
this includes
vastly different levels of support for research and
huge commercial investments & applications
© Tefko Saracevic

14. How to view structure?
by decomposing areas & efforts in research & practice emphasizing
Technology
Information or
People or
Table of content
© Tefko Saracevic

15. Identified with information retrieval (IR)
Part 3. Technology
Identified with information retrieval (IR)
by far biggest effort and investment
international & global
commercial interest large & growing
© Tefko Saracevic

16. Information Retrieval – definition & objective
“ IR: ... intellectual aspects of description of information, ... search, ... & systems, machines...”
Calvin Mooers, 1951
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?
© Tefko Saracevic
© Tefko Saracevic

17. Streams in IR Res. & Dev. 1. Information science:
Services, users, use;
Human-computer interaction;
Cognitive aspects
2. Computer science:
Algorithms, techniques
Systems aspects; evaluation
3. Information industry:
Products, services, Web
search engines – BIG!
Market aspects
Problem:
relative isolation – discussed later
© Tefko Saracevic
© Tefko Saracevic

18. IR research
Started in the US through government support & in information science
Now mostly done within computer science
e.g Special Interest Group on IR, Association for Computing Machinery (SIGIR,ACM)
Gerard Salton
© Tefko Saracevic

19. Contemporary IR research
Spread globally
e.g. major IR research communities emerged in China, Korea, Singapore
Branched outside of information science - “everybody does information retrieval”
search engines, data mining, natural language processing, artificial intelligence, computer graphics …
© Tefko Saracevic

20. Testing in IR
Major component of IR made it strong & affected innovation
Long history – started with Cranfield tests in late 1950’s
Measures – precision & recall based on relevance
Cyril Cleverdon
© Tefko Saracevic

21. Text REtrieval Conference (TREC)
Major research, laboratory effort
Started in 1992,
“support research within the IR community by providing the infrastructure necessary for large-scale evaluation”
Methods
provides large test beds, queries, relevance judgments, comparative analyses
essentially using Cranfield 1960’s methodology
organized around tracks
various topics – changing over years
© Tefko Saracevic

22. TREC impact
International – big impact on creating research communities
Annual conferences
reports, exchange results, foster cooperation
Results
mostly in reports, available at
overviews provided as well
but, only a fraction published in journals
Book (2005):
TREC: Experiment and Evaluation in Information Retrieval Edited by Ellen M. Voorhees and Donna K. Harman
© Tefko Saracevic

23. TREC tracks 2007 116 groups from 20 countries
Genomics
Spam
Blog
Question answering
Enterprise
Million query (new)
Legal
Previous tracks:
ad-hoc ( )
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
© Tefko Saracevic

24. Broadening of IR – sample ever changing, ever new areas added
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
© Tefko Saracevic
© Tefko Saracevic

25. Commercial IR Search engines based on IR
But added many elaborations & significant innovations
dealing with HUGE number of pages fast
countering spamming & page rank games – adversarial IR - combat of algorithms
adding context for searching
Spread & impact worldwide
about 2000 engines in over 160 countries
English was dominant, but not any more
© Tefko Saracevic

26. Commercial IR: brave new world
Large investments & economic sector
hope for big profits, as yet questionable
Leading to proprietary, secret IR
also aggressive hiring of best talent
new commercial research centers in different countries (e.g. MS in China)
Academic research funding is changing
brain drain from academe
Commercial search engines facing many challenges – hiring best talent
and providing brain-drain for academics
© Tefko Saracevic

27. IR successfully effected:
Emergence & growth of the INFORMATION INDUSTRY
Evolution of IS as a PROFESSION & SCIENCE
Many APPLICATIONS in many fields
including on the Web – search engines
Improvements in HUMAN - COMPUTER INTERACTION
Evolution of INTEDISCIPLINARITY
IR has a long, proud history
Table of content
© Tefko Saracevic
© Tefko Saracevic

28. Several areas of investigation;
Part 4. Information
Several areas of investigation;
as basic phenomenon – not much progress
measures as Shannon's not successful
concentrated on manifestations and effects
no recent progress in this basic research
information representation
large area connected with IR, librarianship
metadata
bibliometrics
structures of literature
© Tefko Saracevic

29. What is information?
Intuitively well understood, but formally not well stated
Several viewpoints, models emerged
Shannon: source-channel-destination
signals not content – not really applicable, despite many tries
Cognitive: changes in cognitive structures
content processing & effects
Social: context, situation
information seeking, tasks
© Tefko Saracevic

30. Information in information science: Three senses (from narrowest to broadest)
Information in terms of decision involving little or no cognitive processing
signals, bits, straightforward data - e.g.. inf. theory (Shanon), economics,
Information involving cognitive processing & understanding
understanding, matching texts, Brookes
Information also as related to context, situation, problem-at-hand
USERS, USE,TASK
For information science
(including information retrieval):
third, broadest interpretation necessary
Information does not fit in neat little pockets. On a continuum we can think of information in three senses.
1. - information theory assumes that kind of information, so do economic theories of information that deal with information and decisions. A direct connection between a weather report and decision on taking or not taking an umbrella. Minimum thinking involved. Tall assumptions made. Old anecdote: there was a fire in a building and a bunch of people trapped on a fifth floor. Started paniocking. But there was an economist and he said:” No problem. Let us assume a ladder..”
2. involves cognition. Let me read what Jean Tague Suitcliff said in her book “Measuring information:”
"Information is an intangible that depends on the conceptualization and the understanding of a human being. Records contain words or pictures (tangibles) absolutely, but they contain information relative only to a user Information is associated with a transaction between text and reader, between a record and user."
3. The thrid sense involves still a broader context - the problem-at-hand or situation, the social horizon. This is the sense of information that Nick Belkin is talking about all his professional life. And urging us to adapt.
We need thios third interpretation - others to restricted.
© Tefko Saracevic
© Tefko Saracevic

31. Bibliometrics also related areas:
“… the quantitative treatment of the properties of recorded discourse and behavior pertaining to it.” Fairthorne, 1969
Many quantitative studies & some laws
Bradford’s law, Lotka’s law – regularities
quantity/yield distributions of journals, authors
also related areas:
Scientometrics
covering science in general, not just publications
Infometrics
all information objects
Webmetrics or cybermetrics
using bibliometric techniques to study the web
© Tefko Saracevic
Table of content

32. Part 5. People Professional services Research
in organization – moving toward knowledge management, competitive intelligence
in industry – vendors, aggregators, Internet,
Research
user & use studies
interaction studies
broadening to information seeking studies, social context, collaboration
relevance studies
social informatics
© Tefko Saracevic

33. User & use studies Oldest area Branching into Web use studies
covers many topics, methods, orientations
many studies related to IR
e.g. searching, multitasking, browsing, navigation
theoretical & experimental studies on relevance
Branching into Web use studies
quantitative & qualitative studies
emergence of webmetrics
© Tefko Saracevic

34. Interaction
Traditional IR model concentrates on matching but not on user side & interaction
Several interaction models suggested
Ingwersen’s cognitive, Belkin’s episode, Saracevic’s stratified model
hard to get experiments & confirmation
Considered key to providing
basis for better design
understanding of use of systems
Web interactions: a major new area
© Tefko Saracevic

35. Information seeking
Concentrates on broader context not only IR or interaction, people as they move in life & work
Number of models provided
e.g. Kuhlthau’s information search process, Järvelin’s information seeking
Includes studies of ‘life in the round,’ making sense, information encountering, work life, information discovery
Based on concept of social construction of information
Table of content
© Tefko Saracevic

36. Part 6. Paradigm split in technology - people
Split from early 80’s to date into:
System-centered
algorithms, TREC, search engines
continue traditional IR model
Human-(user)-centered
cognitive, situational, user studies
interaction models, some started in TREC
relevance studies
In the retrivela cluster there are subclusters. The cluster split sometimes starting in the early 1980’s
© Tefko Saracevic
© Tefko Saracevic

37. Human vs. system Human (user) side: System side:
often highly critical, even one-sided
mantra of implications for design
but does not deliver concretely
System side:
mostly ignores user side & studies
‘tell us what to do & we will’
Issue NOT H or S approach
even less H vs. S
but how can H AND S work together
major challenge for the future
If one reads some authors like Dervin and Nilan then one gets an impression that this is a conflict and that there is an alternative away from the dreadful systems approach. It is a backlash to exesses and failures of system approaches and blind spots not only in IR but in general.
All of us who work on the human side, and are not negative and dissmisive of the systems have an obligatory mantra at the beginning and end of our articles reproting results. It is a variation on the theme: the results have important implication for system design. I plead guilty.
But netihter mine nor overwhelming number of other studies have not dddelivered these implivcations in terms of concrete and tested systems designs. When they have it is a rare exception than a rule.
On the system side, they rarely pay attention to the other type of studies. To give an example: Last year SIGIR proceedings have some 37 papers, some 6 or 7 of those, depending how you stretch, have anything to do with human aspects. About 16 to 18 %.
This year is even less.
But my point is not to berate either side.
It is not one against the other, but how can one work with the other.
Real progress in IS and IR will come when we achieve this.
© Tefko Saracevic
© Tefko Saracevic

38. Great separation IR in computer science SIGIR growing a lot:
completely technology oriented
VERY international
not aware at all of the other side
SIGIR growing a lot:
2007 subm. 490, accept. 85, 17%
2006 subm. 399, accept. 74, 19%
1999 subm. 135, accept. 33, 24%
IR, user studies, services in information science
mostly people oriented
aware, but participating less with other side
only a few LIS people come to SIGIR, even fewer SIGIR to ASIST, none to ALA
© Tefko Saracevic

39. Calls vs support
Many calls for user-centered or human-centered design, approaches & evaluation
Number of works discussing it, but few proposing concrete solutions
But: most support for system work
in the digital age support is for digital
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
© Tefko Saracevic

40. Part 7. Relations, alliances, competition
With a number of fields...
Strongest:
1. Librarianship
2. Computer science
© Tefko Saracevic
© Tefko Saracevic

41. Common grounds IS & librarianship share:
Social role in information society
Concern with effective utilization of graphic & other types of records
Research problems related to a number of topics
Transfer to & from information retrieval
© Tefko Saracevic
© Tefko Saracevic

42. Differences IS & librarianship differ in:
Selection & definition of many problems addressed
Theoretical questions & framework
Nature & degree of experimentation
Tools and approaches used
Nature & strength of interdisciplinary relations
© Tefko Saracevic
© Tefko Saracevic

43. One field or two? Point of many debates
Suggest: TWO fields in strong interdisciplinary relations
Not a matter of “better” or “worse” - matters little
common arguments between many fields
Differences matter in:
problem selection & definition
agenda, paradigms
theory, methodology
practical solutions, systems
Best example: IR & library automation
© Tefko Saracevic
© Tefko Saracevic

44. Which? Librarianship. Information science
Library and information science
Libraryandinformationscience
Michael Buckland’s suggestion
Information science
Information sciences
Information
like in the “Information School”
© Tefko Saracevic

45. IS & computer science CS primarily about algorithms
IS primarily about information and its users and use
Not in competition, but complementary
Growing number of computer scientists active in IS – particularly in IR and digital libraries
Concentrating on
advanced IR algorithms & techniques
digital library infrastructure & various domains
human computer interaction
© Tefko Saracevic
© Tefko Saracevic

46. Interaction and IS Two streams: Many studies on:
computer-human interaction
human-computer interaction
Many studies on:
machine aspects of interaction
human variables in interaction
Problems: little feedback between
very hard to evaluate
Web interactions: a major area
Another interdisciplinary area
computers sc., cognitive sc., ergonomics,
Table of content
© Tefko Saracevic
© Tefko Saracevic

47. Part 8. Digital libraries
LARGE & growing area
“Hot” area in R&D
a number of large grants & projects in the US, European Union, & other countries
but “DIGITAL” big & “libraries“ small
“Hot” area in practice
building digital collections, hybrid libraries,
many projects throughout the world
but in the US funding drying out
© Tefko Saracevic

48. Technical problems
Substantial - larger & more complex than anticipated:
representing, storing & retrieving of library objects
particularly if originally designed to be printed & then digitized
operationally managing large collections - issues of scale
dealing with diverse & distributed collections
interoperability; federated searching
assuring preservation & persistence
incorporating rights management
© Tefko Saracevic

49. Research issues understanding objects in DL
representing in many formats
metadata, cataloging, indexing
conversion, digitization
organizing large collections
managing collections, scaling
preservation, archiving
interoperability, standardization
accessing, using, searching
federated searching of distributed collections
evaluation of digital libraries
© Tefko Saracevic

50. DL projects in practice
Heavily oriented toward institutions & their missions
in libraries, but also others
museums, societies, government, commercial
come in many varieties
Spread globally
including digitization
U California, Berkeley’s Libweb “lists over 7700 pages from libraries in over 145 countries”
Spending increasing significantly
often a trade-off for other resources
© Tefko Saracevic

51. Connection? DL research & DL practice presently are conducted
mostly independently of each other
minimally informing each other
and having slight, or no connection
Parallel universes with little connections & interaction, at present
not good for either research or practice
Table of content
© Tefko Saracevic

52. Part 9. Conclusions IS contributions
IS effected handling of information in society
Developed an organized body of knowledge & professional competencies
Applied interdisciplinarity
IR reached a mature stage
penetrated many fields & human activities
Stressed HUMAN in human-computer interaction
© Tefko Saracevic
© Tefko Saracevic

53. Challenges
Adjust to the growing & changing social & organizational role of inf. & related inf. infrastructure
Play a positive role in globalization of information
Respond to technological imperative in human terms
Respond to changes from inf. to communication explosion - bringing own experiences to resolutions, particularly to the web
Join competition with quality
Join DIGITAL with LIBRARIES
© Tefko Saracevic
© Tefko Saracevic

54. Juncture IS is at a critical juncture in its evolution
Many fields, groups ... moving into information
big competition
entrance of powerful players
fight for stakes
To be a major player IS needs to progress in its:
research & development
professional competencies
educational efforts
interdisciplinary relations
Reexamination necessary
© Tefko Saracevic
© Tefko Saracevic

55. Thank you Miró!
Thank you Picasso!
© Tefko Saracevic

56. Thank you Javier & for inviting me!
Gracias
Merci
Hvala
Thank you
Danke
Thank you Javier & for inviting me!
Grazie
© Tefko Saracevic

57. Bibliography
Bates, M. J. (1999). Invisible Substrate of Information Science. Journal of the American Society for Information Science,50,
Bush, V. (1945). As We May Think. Atlantic Monthly, 176, (11), Available:
Hjørland, B. (2000). Library and Information Science: Practice, Theory, and Philosophical Basis. Information Processing & Management, 36 (3),
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),
Saracevic, T. (1999). Information Science. Journal of the American Society for Information Science, 50 (9) Available:
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:
Webber, S. (2003) Information Science in 2003: A Critique. Journal of Information Science, 29, (4),
White, H. and Mc Cain, K. (1998). Visualizing a Discipline: An Author Co-citation Analysis of Information Science Journal of the American Society for Information Science, 49 (4),
© Tefko Saracevic