Perspectives of information science in the digital age

Perspectives of information science in the digital age Tefko Saracevic, Ph. D Rutgers University USA http: //www. scils. rutgers. edu/~tefko © Tefko Saracevic, Rutgers University

Information science: “the science dealing with the efficient collection, storage, and retrieval of information” Webster © Tefko Saracevic, Rutgers University 2

Organization 1. 2. 3. 4. 5. 6. 7. 8. Big picture – problems, solutions, social place Underlying stuff – theories, phenomena Structure – what is inside stuff Systems stuff – information retrieval, relevance People stuff – users, use, seeking, context Alliances, competition – the OUCH stuff Digital libraries – whose are they anyhow? Conclusions – Will we have a field stuff? © Tefko Saracevic, Rutgers University 3

1. The big picture Problems addressed Bit of history: Vannevar Bush (1945): l Problem: “. . . the massive task of making more accessible of a bewildering store of knowledge. ” l still with us & growing Basic problem of information science: Information explosion today: PLUS Communication explosion © Tefko Saracevic, Rutgers University 4

… solution Bush: “Memex. . . association of ideas. . . duplicate mental processes artificially. ” Technological fix to problem Still with us: technological determinant l tail that wags the dog © Tefko Saracevic, Rutgers University 5

Problems & solutions: SOCIAL CONTEXT Professional practice AND scientific inquiry related to: l l Effective communication of knowledge records ‘literature’ - among humans in the context of social, organizational, & individual need for and use of information. “modeling the world of publications with a practical goal of being able to deliver their content to inquirers [users] on demand. ” White & Mc. Cain Taking advantage of modern information technology © Tefko Saracevic, Rutgers University 6

Elaboration Knowledge records = texts, sounds, images, multimedia. . . literature in given domains l l content-bearing structures symbol manipulations are content neutral - infrastructural to inf. sc. Communication = human-computer-literature interface l study of inf. science is the interface between people & literatures Inf. need, seeking, and use = reason d'être Effectiveness = relevance, utility © Tefko Saracevic, Rutgers University 7

General characteristics - leitmotifs ¶ Intedisciplinarity - relations with a number of fields · Technological imperative - driving force, as in many modern fields ¸ Information society - social context and role in evolution - shared with many fields © Tefko Saracevic, Rutgers University 8

2. Underlying stuff What is information? Intuitively well understood, but formally? ? l Several viewpoints, models Shannon: source-channel-destination l grapes into wine Cognitive: changes in cognitive structures l water into wine Social: context is the king l whatever into wine to get drunk © Tefko Saracevic, Rutgers University 9

K(S) + I = K(S + S) (Brookes) Information [structured information] when operating on a knowledge structure produces an effect whereby the knowledge structure is changed Potential information added (Ingwersen) Actually, it states the problem – l l l “unoperational” in information systems involves mental events only constructivists rejected it © Tefko Saracevic, Rutgers University 10

Information in inf science: Three senses (from narrowest to broadest) ¶ Inf. in terms of decision involving little or no cognitive processing l signals, bits, straightforward data - e. g. . inf. theory, economics · Inf. involving cognitive processing & understanding l understanding, matching texts ¸ Inf. also as related to situation, task, problem-athand : USERS, USE For information science (incl. information retrieval): l third, broadest interpretation © Tefko Saracevic, Rutgers University 11

The biggest problem MEASUREMENT © Tefko Saracevic, Rutgers University 12

3. Structure Specialties (White & Mc. Cain) In desc. order of author co-citation; (120 authors, 24 years): l experimental retrieval l citation analysis l practical retrieval l bibliometrics l library systems, automation l user studies and theory l scientific communication l OPAC’s l general - other disciplines l indexing theory l communication theory © Tefko Saracevic, Rutgers University 13

Structure or oeuvres Two large sub-disciplines: Ê “Domain” cluster: analytical study of literatures, their structure, communication, social context, uses Ë Retrieval cluster: human-literature interface: IR systems (largest); interaction; library systems, OPACs, user studies l within each sub-clusters, eras l e. g. . Salton & post-Salton era Largely not connected l l some authors in both, migrating BUT: lacking integrating works, authors, texts - big payout © Tefko Saracevic, Rutgers University 14

Paradigm split in retrieval cluster Split from early 80’s to date ¶ System-centered algorithms, TREC l continue traditional IR model l · Human-(user)-centered cognitive, situational, user studies l interaction models, some started in TREC l Calls for user-centered approaches & evaluation But: most support for system work l in the digital age support is for digital © Tefko Saracevic, Rutgers University 15

Human vs. system Human (user) side: l l l often highly critical, even one-sided mantra of implications for design but does not deliver concretely System side: l l mostly ignores user side & studies ‘tell us what to do & we will’ Issue NOT H or S approach l l l even less H vs. S but how can H AND S work together major challenge for the future © Tefko Saracevic, Rutgers University 16

4. Systems stuff Information Retrieval “ IR: . . . intellectual aspects of description of inf. , . . . search, . . . & systems, machines. . . ” Calvin Mooers, 1951 How to provide users with useful 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, Rutgers University 17

Streams in IR Res. & Dev. 1. Information science: l l l Services, users, use; Human-computer interaction; Cognitive aspects 2. Computer science: l l Algorithms, techniques Systems aspects 3. Information industry: l l Products, services, Web Market aspects Problems: . . . relative isolation. . . inadequate cooperation, transfer © Tefko Saracevic, Rutgers University 18

IR successfully effected: Emergence & growth of the INFORMATION INDUSTRY Evolution of IS as a PROFESSION & SCIENCE Many APPLICATIONS in many fields l including on the Web – search engines Improvements in HUMAN - COMPUTER INTERACTION Evolution of INTEDISCIPLINARITY IR has a long, proud history © Tefko Saracevic, Rutgers University 19

Broadening of IR OPACs (Online Public Access Catalogs) Natural language processing Summarization Metadata representations Text “understanding” Hypertext, hypermedia Multimedia - images, sounds. . . l image IR, music IR Many human-computer interactions Web search engines © Tefko Saracevic, Rutgers University 20

5. People stuff Quite a few areas Professional services l l in organization – moving toward knowledge management, competitive intelligence in industry – vendors, aggregators, Internet, Research l l l user & use studies interaction studies broadening to information seeking studies, social context, collaboration relevance studies social informatics © Tefko Saracevic, Rutgers University 21

User & use studies Oldest area covers many topics, methods, orientations l many studies related to IR l l e. g. searching, multitasking, browsing, navigation Branching into Web use studies quantitative & qualitative studies l emergence of webmetrics l © Tefko Saracevic, Rutgers University 22

Interaction Traditional IR model concentrates on matching not user side & interaction Several interaction models suggested l l Ingwersen’s cognitive, Belkin’s episode, Saracevic’s stratified model hard to get experiments & confirmation Considered key to providing basis for better design l understanding of use of systems l Web interactions a major new area © Tefko Saracevic, Rutgers University 23

Relevance Effectiveness in IR = relevance l thus, relevance became a key notion l and a key headache A number of studies & reviews on: Nature: Framework, base? l Manifestations: Contexts? Typologies? l Behavior: Variables? Observations? l Effects: Use? Evaluation? l © Tefko Saracevic, Rutgers University 24

Manifestations (types) of relevance System or algorithmic relevance l relation between query & objects (‘texts’) retrieved or failed to retrieve Topical or subject relevance Cognitive relevance or pertinence Situational relevance or utility l relation between the situation, task or problem at hand & texts Motivational or affective relevance l intent, goals, & motivation of user & “texts” Manifestations interact dynamically © Tefko Saracevic, Rutgers University 25

Information seeking Concentrates on broader context not only IR or interaction, people as they move in life & work Number of models provided l e. g. Kuhlthau’s stages, Vakkari’s problem situation, task complexity Includes studies of ‘life in the round, ’ making sense, information encountering, work life, information discovery Based on concept of social construction of information © Tefko Saracevic, Rutgers University 26

6. Alliances, competition Relations With a number of fields. . . Strongest: 1. Librarianship 2. Computer science © Tefko Saracevic, Rutgers University 27

Librarianship [Library is]. . . “contributing to the total communication system in society. Created to maximize the utility of graphic record for the benefits of society. . . it achieves that goal by working with the individual and through the individual it reaches society. ” J. H. Shera, 1972 © Tefko Saracevic, Rutgers University 28

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, Rutgers University 29

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, Rutgers University 30

One field or two? Point of many debates Suggest: TWO fields in strong interdisciplinary relations Not a matter of “better” or “worse” - matters little l common arguments between many fields Differences matter in: l l problem selection & definition agenda, paradigms theory, methodology practical solutions, systems Best example: IR & library automation © Tefko Saracevic, Rutgers University 31

Which? Librarianship. Information science Library and information science Libraryandinformationscience Information sciences Information l like in the “Information School” © Tefko Saracevic, Rutgers University 32

Computer science “systematic study of algorithmic processes that describe and transfer information. . The fundamental question in computing is: ‘What can be (efficiently) automated’. ” Denning et al. , 1989 © Tefko Saracevic, Rutgers University 33

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 l l l advanced IR algorithms & techniques digital library infrastructure & various domains human computer interaction © Tefko Saracevic, Rutgers University 34

Human-computer interaction (HCI) “ Human computer interaction is a discipline concerned with the design, evaluation and implementation of interactive computing systems for human use and with the study of major phenomena surrounding them. ” ACM SIGCHI, 1993 Another interdisciplinary area l computers sc. , cognitive sc. , ergonomics, . . . © Tefko Saracevic, Rutgers University 35

Interaction and IS Two streams: l l computer-human interaction human-computer interaction Modern IR is interactive l BUT: difference between retrieval engine & retrieval interface Many studies on: l l machine aspects of interaction human variables in interaction Problem: little feedback between Interaction very hard to evaluate - few methods yet © Tefko Saracevic, Rutgers University 36

7. Digital libraries LARGE & growing area “Hot” area in R&D a number of large grants & projects in the US, European Union, & other countries l but “DIGITAL” big & “libraries“ small l “Hot” area in practice building digital collections, hybrid libraries, l many projects throughout the world l © Tefko Saracevic, Rutgers University 37

Technical problems Substantial - larger & more complex than anticipated: l representing, storing & retrieving of library objects l l l operationally managing large collections - issues of scale dealing with diverse & distributed collections l l l particularly if originally designed to be printed & then digitized interoperability assuring preservation & persistence incorporating rights management © Tefko Saracevic, Rutgers University 38

Digital Library Initiatives in the US (DLI) Research consortia under National Science Foundation l l DLI 1: 1994 -98, 3 agencies, $24 M, six large projects DLI 2: 1999 -2006, 8 agencies, $60+M, 77 large & small projects in various categories ‘digital library’ not defined to cover many topics & stretch ideas l not constrained by practice © Tefko Saracevic, Rutgers University 39

European Union DELOS Network of Excelence on Digital Libraries l many projects throughout European Union l heavily technological many meetings, workshops l resembles DLIs in the US l well funded, long range l © Tefko Saracevic, Rutgers University 40

Research issues l understanding objects in DL l l l l l representing in many formats non-textual materials metadata, cataloging, indexing conversion, digitization organizing large collections managing collections, scaling preservation, archiving interoperability, standardization accessing, using, © Tefko Saracevic, Rutgers University 41

DL projects in practice Heavily oriented toward institutions Assoc of Res Libraries (ARL) database: 427 DL projects in 13 countries l 374 in the US l 51% in universities; 24% fed govmt; 9% hist societies; 6% regional … l 84% are explicitly retrospective; 16% technological l 1 listed from DLI (Illinois) l no connection with DLI projects l © Tefko Saracevic, Rutgers University 42

Agendas Most DL research agenda is set from top down l l from funding agencies to projects imprint of the computer science community's interest & vision Most DL practice agendas are set from bottom up l l from institutions, incl. many libraries imprint of institutional missions, interests & vision l l providing access to specialized materials and collections from an institution (s) that are otherwise not accessible covering in an integral way a domain with a range of sources © Tefko Saracevic, Rutgers University 43

Connection? DL research & DL practice presently are conducted mostly independent of each other, l minimally informing each other, l & having slight, or no connection l Parallel universes with little connections & interaction © Tefko Saracevic, Rutgers University 44

8. Conclusions IS contributions IS effected handling of inf. in society Developed an organized body of knowledge & professional competencies Applied interdisciplinarity IR reached a mature stage IR penetrated many fields & human activities Stressed HUMAN in human-computer interaction © Tefko Saracevic, Rutgers University 45

Challenges Adjust to the growing & changing social & organizational role of inf. & related 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 INTERNET Join competition with quality Join DIGITAL with LIBRARIES © Tefko Saracevic, Rutgers University 46

Juncture IS is at a critical juncture in its evolution Many fields, groups. . . moving into information l l l big competition entrance of powerful players fight for stakes To be a major player IS needs to progress in its: l l research & development professional competencies educational efforts interdisciplinary relations Reexamination necessary © Tefko Saracevic, Rutgers University 47

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