1. © 2009 Tefko Saracevic 1 Information Science: Where does it come from and where is it going? Tefko Saracevic, PhD School of Communication, & Information Rutgers University New Brunswick, New Jersey USA http://www.scils.rutgers.edu/~tefko

2. © 2009 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

3. actually, it all started long ago In China: Wang Zhen Wang Zhen developed wooden movable type & published first book in 1313 In Europe  Johannes Gutenberg is credited with being the first European to use movable type printing around 1439 Johannes Gutenberg movable type printing © 2009 Tefko Saracevic 3

4. 4 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.Digital libraries – whose are they anyhow?Digital libraries 7.Conclusions – big questions for the futureConclusions

5. © 2009 Tefko Saracevic 5 Part 1. The big picture Problems addressed  Bit of history: Vannevar Bush (1945):Vannevar Bush (1945):  Defined problem as “... the massive task of making more accessible of a bewildering store of knowledge.”  Problem still with us & growing 1890-1974

6. © 2009 Tefko Saracevic 6 … solution  Bush suggested a machine: “Memex... association of ideas... duplicate mental processes artificially.”  Technological fix to problem  Still with us: technological determinant

7. © 2009 Tefko Saracevic 7 At the base of information science: Problem of information explosion 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  Dealing with effects of this abundance

8. © 2009 Tefko Saracevic 8 technological solution, BUT … applying technology to solving problems of effective use of information BUT: from a HUMAN & SOCIAL and not only TECHNOLOGICAL perspective

9. © 2009 Tefko Saracevic 9 or a symbolic model Information Technology People

10. © 2009 Tefko Saracevic 10 Problems & solutions: SOCIAL CONTEXT Information science:  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

11. © 2009 Tefko Saracevic 11 Elaboration  Knowledge records =  content-bearing structures  texts, sounds, images, multimedia, web... ‘literature’ in given domains  Communication =  human-information interaction  study of information science is the interface between people & information  Information need, seeking, and use =  reason d'être  Effectiveness =  relevance, utility

12. © 2009 Tefko Saracevic 12 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

13. © 2009 Tefko Saracevic 13 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

14. © 2009 Tefko Saracevic 14 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

15. © 2009 Tefko Saracevic 15 How to view structure? by decomposing areas & efforts in research & practice emphasizing Technology Information or People or

16. Three big questions for information science (Bates, 1999)  The design question: [Technology] How can access to recorded information be made most rapid and effective?  The physical question: [Information] What are the features and laws of the recorded information universe?  The social question: [People] How do people relate to, seek and use information? © 2009 Tefko Saracevic 16 Table of content

17. © 2009 Tefko Saracevic 17  Identified with information retrieval (IR)  by far biggest effort and investment  international & global  commercial interest large & growing Part 3. Technology

18. © 2009 Tefko Saracevic 18 Information Retrieval – definition & objective “ IR:... intellectual aspects of description of information,... search,... & systems, machines...” Calvin MooersCalvin 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? 1919-1994

19. © 2009 Tefko Saracevic 19 Streams in IR research & development 1. Information science:  Services, users, use;  Human-computer interaction;  Cognitive aspects 2. Computer science:  Algorithms, techniques  Systems aspects 3. Information industry:  Products, services, Web, search engines  Market aspects  Problem:  relative isolation between these streams

20. © 2009 Tefko Saracevic 20 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)Special Interest Group on IR, Gerard Salton 1927-1995

21. © 2009 Tefko Saracevic 21 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, natural language processing, data mining, artificial intelligence, organization – ontologies …

22. © 2009 Tefko Saracevic 22 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 1914-1997

23. © 2009 Tefko Saracevic 23 Talking about relevance  Major objective of IR is to retrieve RELEVANT information  But what is “relevance?”  Spurred many research studies in IS  manifestations, models, theories,  experimental studies on people behavior, effects, criteria in judgments, variability, clues …  Still major criterion in search engines

24. © 2009 Tefko Saracevic 24 Text REtrieval Conference (TREC)(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

25. © 2009 Tefko Saracevic 25 TREC impact  International – big impact on creating research communities, incl. in Asia  Annual conferences  reports, exchange results, foster cooperation  Results  mostly in reports, available at http://trec.nist.gov/pubs.html http://trec.nist.gov/pubs.html  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 TREC: Experiment and Evaluation in Information RetrievalEllen M. VoorheesDonna K. Harman

26. © 2009 Tefko Saracevic 26 Broadening of IR – ever changing, ever new areas added  Cross language IR (CLIR)  Natural language processing (NLP IR)  Specific media IR: music, spoken language, image, video, multimedia  IR for bioinformatics, genomics, law …  Categorization, clustering, filtering  Information summarization & extraction  Question answering  Machine learning  IR & database search – XML retrieval; structured queries  Web IR; Web search engines  Digital libraries

27. © 2009 Tefko Saracevic 27 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

28. © 2009 Tefko Saracevic 28 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 & IR facing many challenges  lead in innovations

29. © 2009 Tefko Saracevic 29 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

30. © 2009 Tefko Saracevic 30 Part 4. Information  Several areas of investigation;  as basic phenomenon – not much progress  measures as Shannon's not successful  concentrated on manifestations and effects  information representation  large area connected with IR, librarianship  metadata  bibliometrics scientometrics, informetrics, webometrics  structures of literature – authors, journals…  impact of authors, journals, institutions …

31. © 2009 Tefko Saracevic 31 What is information? Intuitively well understood, but formally not well stated  Several viewpoints, models emerged  Signals: transmission source-channel-destination  signals not content – not really applicable, despite many tries  Cognitive: changes in cognitive structures  content processing & effects  Social: context, situation dependent  information seeking, tasks

32. © 2009 Tefko Saracevic 32 Information in information science: Three senses (from narrowest to broadest) 1.Information in terms of decision involving little or no cognitive processing  signals, bits, straightforward data - e.g.. inf. theory (Shanon), economics, 2.Information involving cognitive processing & understanding  understanding, changes in cognitive states 3.Information also as related to context, situation, problem-at-hand  users, use, task For information science (including information retrieval): third, broadest interpretation necessary

33. © 2009 Tefko Saracevic 33 Bibliometrics “… 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  Informetrics  all information objects  Webometrics or cybermetrics  using bibliometric techniques to study the web

34. Major branches of bibliometrics Relational - older  Patterns, structures, relations, mappings  where bibliometrics started  Data on what was observed  e.g. no. of articles/citations by/to an author; no. of journals with articles relevant to a topic; no. of articles/citations in/to a journal …  Used for description, mapping of relations & prediction Evaluative - newer  Impacts, effects  where bibliometrics became a big deal in many arenas  Data from what was observed but looking for  measures of impact, prominence, ranking …  Discovers who’s up & how much up  Used for decisions, policies 34 © 2009 Tefko Saracevic

35. Major bibliometric factors for evaluation of academic performance For individuals  Number of publications  in peer reviewed journals  impact factor of those journals  Citation tracking  The h-index  combines no. of publications & no. of citations For institutions  Total no. of publications  Total no. of citations  Institutional impact factor  Various ratios - per faculty, project … 35 © 2009 Tefko Saracevic

36. Example: University rankings  Times Higher Education ranking: QS World University Rankings 2008 - Top 400 Universities http://www.topuniversities.com/worlduniversityrankings/ results/2008/overall_rankings/fullrankings/ http://www.topuniversities.com/worlduniversityrankings/ results/2008/overall_rankings/fullrankings/  Shanghai ranking: Academic Ranking of World Universities – 2007 - Shanghai Jiao Tong University http://www.arwu.org/rank/2007/ranking2007.htm http://www.arwu.org/rank/2007/ranking2007.htm  Miscellaneous Information on University Rankings http://www.arwu.org/rank/2008/200810/ARWU2008Resources.htmhttp://www.arwu.org/rank/2008/200810/ARWU2008Resources.htm  Leiden ranking: Top 100 & 250 universities, Europe & world, 2008 - Leiden University, Netherlands http://www.cwts.nl/ranking/LeidenRankingWebSite.html http://www.cwts.nl/ranking/LeidenRankingWebSite.html 36© 2009 Tefko Saracevic

37. SCImago Journal & Country Rank (SJR) SCImago Journal & Country Rank (SJR) a great resource – from Spain 37 © 2009 Tefko Saracevic

38. Used in a variety of functions & areas  In collection development identifying the most-useful materials: by analyzing circulation records; journal / e-journal usage statistics; etc.  In information retrieval identifying top-ranked documents, authors: those most highly-cited; most highly co-cited; most popular; etc.  In the sociology of knowledge identifying structural & temporal relationships between documents, authors, research areas, universities etc.  In policy making justifying, managing or prioritizing support for course of action in a number of areas e.g. science policy, institutional policy, promotion & tenure, grants, support for journals, evaluation of institutions 38© 2009 Tefko Saracevic Table of content

39. 39 Part 5. People  Research  user & use studies  interaction studies  broadening to information seeking studies, social context, collaboration  relevance studies  social informatics  Professional services  in organization – moving toward knowledge management, competitive intelligence  in industry – vendors, aggregators, Internet, © 2009 Tefko Saracevic

40. 40 User & use studies  Oldest area  covers many topics, methods, orientations  many studies related to IR  e.g. searching, multitasking, browsing, navigation  theoretical & experimental studies on relevance relevance  Branching into Web use studies  quantitative & qualitative studies  emergence of webmetrics

41. © 2009 Tefko Saracevic 41 Interaction and IS  Three streams:  computer-human interaction  human-computer interaction  human-information interaction  Many studies on:  machine aspects of interaction  human variables in interaction  interaction with information  Web interactions: a major area  Another interdisciplinary area  computers science, information science, cognitive science, ergonomics…

42. © 2009 Tefko Saracevic 42 Interaction & IR  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 Ingwersen’s cognitive 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

43. © 2009 Tefko Saracevic 43 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 seekingKuhlthau’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

44. © 2009 Tefko Saracevic 44 Part 6. Digital libraries  LARGE & growing area – many in IS involved, but also in computer science, & other fields (e.g. Perseus – classics)Perseus  “Hot” area in R&D  a number of large grants & projects in the US, European Union, & other countries  but “DIGITAL” big & “libraries“ small  but in the US & Europe funding is drying out  “Hot” area in practice  building & managing digital collections, hybrid libraries, digitizing, preservation  many projects throughout the world

45. © 2009 Tefko Saracevic 45 Technical problems  Substantial - larger & more complex than anticipated e.g.:  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

46. © 2009 Tefko Saracevic 46 Research issues  understanding objects in DL  representing in many formats  metadata, automating representation  conversion, digitization  organizing large collections  managing collections, scaling  preservation, archiving  interoperability, standardization  accessing, using, searching  federated searching of distributed collections  evaluation of digital libraries

47. © 2009 Tefko Saracevic 47 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  Spending increasing significantly  most often a trade-off for other resources  U California, Berkeley’s Libweb “lists over 7700 pages from libraries in over 146 countries”Libweb

48. © 2009 Tefko Saracevic 48 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

49. © 2009 Tefko Saracevic 49 Part 7. 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

50. © 2009 Tefko Saracevic 50 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

51. © 2009 Tefko Saracevic 51 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 keep progressing in its:  research & development  professional competencies  educational efforts  interdisciplinary relations  Reexamination necessary

52. © 2009 Tefko Saracevic 52 Thank you Miró!Miró! Thank you Picasso!Picasso!

53. © 2009 Tefko Saracevic 53 & Prof. Sam Chu for inviting me! Thank you:

54. © 2009 Tefko Saracevic 54 Selective 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.