© Tefko Saracevic 1 Information Science 2005 Tefko Saracevic, PhD School of Communication, Information and Library Studies Rutgers University New Brunswick, New Jersey USA

© Tefko Saracevic 2 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

© Tefko Saracevic 3 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 Table of content

© Tefko Saracevic 4 … 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

© Tefko Saracevic 5 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

© Tefko Saracevic 6 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 7 or a symbolic model Information Technology People

© Tefko Saracevic 8 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

© Tefko Saracevic 9 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

© Tefko Saracevic 10 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

© Tefko Saracevic 11 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

© Tefko Saracevic 12 How to view structure? by decomposing areas & efforts in research & practice emphasizing Technology Informatio n or People or Table of content

© Tefko Saracevic 13 α Identified with information retrieval (IR) β by far biggest effort and investment β international & global β commercial interest large & growing Part 3. Technology

© Tefko Saracevic 14 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?

© Tefko Saracevic 15 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 …

© Tefko Saracevic 16 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

© Tefko Saracevic 17 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 β overviews provided as well β but, only a fraction published in journals or books

© Tefko Saracevic 18 TREC tracks 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 ( ) 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

© Tefko Saracevic 19 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

© Tefko Saracevic 20 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

© Tefko Saracevic 21 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

© Tefko Saracevic 22 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

© Tefko Saracevic 23 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

© Tefko Saracevic 24 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

© Tefko Saracevic 25 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

© Tefko Saracevic 26 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

© Tefko Saracevic 27 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

© Tefko Saracevic 28 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

© Tefko Saracevic 29 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

© Tefko Saracevic 30 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

© Tefko Saracevic 31 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

© Tefko Saracevic 32 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

© Tefko Saracevic 33 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

© Tefko Saracevic 34 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

© Tefko Saracevic 35 US: Digital Library Initiatives α Consortia under National Science Foundation funding research β DLI 1: , 3 agencies, $24M, 6 large projects DLI 1 β DLI 2: , 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

© Tefko Saracevic 36 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

© Tefko Saracevic 37 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

© Tefko Saracevic 38 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

© Tefko Saracevic 39 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

© Tefko Saracevic 40 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 41 Challenges α Adjust to the growing & changing social & organizational role of information & related information infrastructure α Play a positive role in globalization of information α Respond to technological imperative in human terms α Respond to changes from information to communication explosion β bringing own experiences to resolutions, particularly to the web α Join competition with quality α Join DIGITAL with LIBRARIES

© Tefko Saracevic 42 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

© Tefko Saracevic 43 Thank you Miró!Miró!

© Tefko Saracevic 44

© Tefko Saracevic 45 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),