2. © Tefko Saracevic 1 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

3. © 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 Merriam-Webster

4. © Tefko Saracevic 3 Organization of presentation 1.Big picture – problems, solutions, social placeBig picture 2.Structure – main areas in research & practiceStructure 3.Technology – information retrieval – largest partTechnology 4.Information – representation; bibliometricsInformation 5.People – users, use, seeking, contextPeople 6.Paradigm split – distancing of areasParadigm split 7.Digital libraries – whose are they anyhow?Digital libraries 8.Conclusions – big questions for the futureConclusions

5. © Tefko Saracevic 4 Part 1. The big picture Problems addressed α Bit of history: Vannevar Bush (1945):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

6. © Tefko Saracevic 5 … 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

7. © Tefko Saracevic 6 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

8. © 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

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

10. © Tefko Saracevic 9 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

11. © Tefko Saracevic 10 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

12. © Tefko Saracevic 11 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.”

13. © Tefko Saracevic 12 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

14. © Tefko Saracevic 13 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

15. © Tefko Saracevic 14 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

16. © Tefko Saracevic 15 How to view structure? by decomposing areas & efforts in research & practice emphasizing Technology Informatio n or People or Table of content

17. © Tefko Saracevic 16 α Identified with information retrieval (IR) β by far biggest effort and investment β international & global β commercial interest large & growing Part 3. Technology

18. © Tefko Saracevic 17 Information Retrieval – definition & objective “ IR:... intellectual aspects of description of information,... search,... & systems, machines...” Calvin MooersCalvin 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?

19. © Tefko Saracevic 18 Streams in IR Res. & Dev. 1. Information science: β Services, users, use; β Human-computer interaction; β Cognitive aspects 2. Computer science: β Algorithms, techniques β Systems aspects 3. Information industry: β Products, services, Web β Market aspects α Problem: β relative isolation – discussed later

20. © Tefko Saracevic 19 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 …

21. © Tefko Saracevic 20 Text REtrieval Conference (TREC)(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 large- scale 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

22. © Tefko Saracevic 21 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 http://trec.nist.gov/pubs.html β overviews provided as well β but, only a fraction published in journals or books

23. © Tefko Saracevic 22 TREC tracks 2004 103 groups from 21 countries α Genomics with 4 sub tracks α HARD (High Accuracy 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 α Previous tracks: β ad-hoc (1992-1999) β routing (92–97) β interactive (94-02) β filtering (95-02) β cross language (97-02) β speech (97-00) β Spanish (94-96) β video (00-01) β Chinese (96-97) β query (98-00) β and a few more run for two years only

24. © Tefko Saracevic 23 Broadening of IR – ever changing, ever new areas added α Cross language IR (CLIR) α Natural language processing (NLP IR) α Music IR (MIR) α Image, video, multimedia retrieval α Spoken language retrieval α IR for bioinformatics and genomics α Summarization; text extraction α Question answering α Many human-computer interactions α XML IR α Web IR; Web search engines α IR in context – big area for major search engines & newer research

25. © Tefko Saracevic 24 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

26. © Tefko Saracevic 25 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 :Amit Singhal presentationAmit Singhal

27. © Tefko Saracevic 26 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 Table of content

28. © Tefko Saracevic 27 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?What is information?

29. © Tefko Saracevic 28 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

30. © Tefko Saracevic 29 Cognitive – basic idea: K(S) + Δ I = K(S + Δ S) (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

31. © Tefko Saracevic 30 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 β data, documents with quality of imparting information - tangible γ refers to potential information conveyed from objects

32. © Tefko Saracevic 31 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

33. © Tefko Saracevic 32 Bibliometrics “… the quantitative treatment of the properties of recorded discourse and behavior pertaining to it.” Fairthorne 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 Table of content

34. © Tefko Saracevic 33 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

35. © Tefko Saracevic 34 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

36. © Tefko Saracevic 35 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 Ingwersen’s cognitive 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

37. © Tefko Saracevic 36 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 basedKuhlthau’s stagesJärvelin’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 Table of content

38. © Tefko Saracevic 37 Part 6. Paradigm split in technology - people 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

39. © Tefko Saracevic 38 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

40. © Tefko Saracevic 39 Calls vs support α Many calls for user-centered or human- centered 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. Ingerwersen, P Table of content

41. © Tefko Saracevic 40 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

42. © Tefko Saracevic 41 Technical problems α Substantial - larger & more complex than anticipated: β representing, storing & retrieving of library objects 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

43. © Tefko Saracevic 42 US: Digital Library Initiatives α Consortia under National Science Foundation funding research β DLI 1: 1994-98, 3 agencies, $24M, 6 large projects DLI 1 β DLI 2: 1999-2006, 8 agencies, $60+M, 77 large & small projects in various categories DLI 2: β joint international projectsinternational projects β National Science, Mathematics, Engineering, and Technology Education Digital Library γ some 200 demonstration & development projects200 demonstration & development projects α Funding pretty much over by 2005 β funding now in related areas

44. © Tefko Saracevic 43 EU: DELOS α DELOS Network of Excellence on Digital Libraries DELOS β many projects throughout European Union γ heavily technological β many meetings, workshops β to some degree resembles DLIs in the US β well funded, long range β unlike in the US support still going on

45. © Tefko Saracevic 44 Research issues α understanding objects in DL β representing in many formats α metadata, cataloging, indexing α conversion, digitization α organizing large collections α managing collections, scaling α preservation, archiving α interoperability, standardization α accessing, using, searching β federated searching of distributed collections α evaluation of digital libraries evaluation

46. © Tefko Saracevic 45 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”Libweb α Spending increasing significantly β often a trade-off for other resources

47. © Tefko Saracevic 46 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

48. © Tefko Saracevic 47 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 Table of content

49. © Tefko Saracevic 48 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

50. © Tefko Saracevic 49 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

51. © Tefko Saracevic 50 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 research & development β professional competencies β educational efforts β interdisciplinary relations α Reexamination necessary

52. © Tefko Saracevic 51 Thank you Miró!Miró!

53. © Tefko Saracevic 52

54. © Tefko Saracevic 53 Bibliography Bates, M. J. (1999). Invisible Substrate of Information Science. Journal of the American Society for Information Science,50, 1043- 1050. Bush, V. (1945). As We May Think. Atlantic Monthly, 176, (11), 101- 108. Available: http://www.theatlantic.com/unbound/flashbks/computer/bushf.htm 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 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 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.