Tefko Saracevic 1
Information retrieval (IR)
Basics, models, interactions
[email protected]; http://comminfo.rutgers.edu/~tefko/
Tefko Saracevic
• Information retrieval (IR) is at the heart of ALL indexing & abstracting databases, information resources, and search engines– all work on basis of IR algorithms and procedures
• Contemporary IR is also interactive – to such a degree that pragmatically IR can not be separated from interaction
• As a searcher you will constantly use IR, thus you have to be knowledgeable about it
Central ideas
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Tefko Saracevic
1. Information retrieval (IR)2. Matching algorithms: Exact match & best match3. Strength & weaknesses4. IR Interaction & interactive models
ToC
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Tefko Saracevic
Definitions. Traditional model1. Information retrieval
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Tefko Saracevic
Calvin Mooers (1919-1994) coined the term “Information retrieval embraces the intellectual aspects
of the description of information and its specification for search, and also whatever systems, techniques, or machines are employed to carry out the operation.”
Mooers, 1951
Information retrieval (IR)- original definition
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Tefko Saracevic
IR:Objective & problems
Objectives:Provide users with effective access to & interaction with
information resources.Retrieve information or information objects that are relevant
Problems addressed:1. How to organize information intellectually?2. How to specify search & interaction intellectually?3. What systems & techniques to use for those
processes?
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Tefko Saracevic
IR models• Model depicts, represents what is involved - a choice of
features, processes, things for consideration• Several IR models used over time
– traditional: oldest, most used, shows basic elements involved– interactive: more realistic, favored now, shows also
interactions involved; several models proposed
• Each has strengths, weaknesses• We start with traditional model to illustrate many points
- from general to specific examples
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Tefko Saracevic
Description of traditional IR model
• It has two streams of activities – one is the systems side with processes performed by the system– other is the user side with processes performed by users &
intermediaries (you)– these two sides led to “system orientation” & “user orientation”– in system side automatic processing is done; in user side human
processing is done
• They meet at the matching process– where the query is fed into the system and system looks for documents
that match the query
• Also feedback is involved so that things change based on results – e.g. query is modified & new matching done
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Tefko Saracevic
Traditional IR model
File organizationindexed documents
Acquisitiondocuments, objects
Representationindexing, ...
Probleminformation need
Representationquestion
Querysearch formulation
Matchingsearching
Retrieved objects
System User
feed
back
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Tefko Saracevic
• Content: What is in databases– In Dialog first part of blue sheets: File Description, Subject
Coverage; in Scopus Subject Areas
• Selection of documents & other objects from various sources - journals, reports … – In Blue Sheets: Sources; in Scopus Sources
• Mostly text based documents– Full texts, titles, abstracts ...– But also: data, statistics, images (e.g. maps, trade marks) ...
Acquisitionsystem side
Importance: Determines contents of databases Key to file selection in searching !!!
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Tefko Saracevic
• Indexing – many ways :– free text terms (even in
full texts)– controlled vocabulary -
thesaurus– manual & automatic
techniques
• Abstracting; summarizing• Bibliographic description:
– author, title, sources, date…– metadata
• Classifying, clustering • Organizing in fields & limits
– in Dialog: Basic Index, Additional Index. Limits
– in Scopus pull down menus
Representationof documents, objects …
system side
Basic to what is available for searching & displaying
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Tefko Saracevic
File organizationsystem side
As mentioned:• Sequential
– record (document) by record
• Inverted – term by term; list of records under each term
• Combination: indexes inverted, documents sequential
• When citation retrieved only, need for document files or document delivery
Enables searching & interplay between types of files
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Tefko Saracevic
Problemuser side
• Related to user’s task, situation, problem at hand – vary in specificity, clarity
• Produces information need– ultimate criterion for effectiveness of retrieval
• how well was the need met?
• Inf. need for the same problem may change, evolve, shift during the IR process - adjustment in searching– often more than one search for same problem over time
• you will experience this in your term project
Critical for examination in interview
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Tefko Saracevic
• Focus toward– deriving search terms &
logic– selection of files,
resources• Subject to feedback
changes • Critical roles of
intermediary - you
Representationquestion – user side
Non-mediated: end user aloneMediated: intermediary + user
– interviews; human-human interaction
• Question analysis– selection, elaboration of
terms– various tools may be
used • thesaurus, classification
schemes, dictionaries, textbooks, catalogs …
Determines search specification - a dynamic process14
Tefko Saracevic
• Search strategy - selection of:– search terms & logic– possible fields, delimiters – controlled & uncontrolled
vocabulary– variations in tactics
• Reiterations from feedback – several feedback types: relevance
feedback, magnitude feedback ...– query expansion & modification
Querysearch formulation – user side
• Translation into systems requirements & limits – start of human-computer
interaction
• Selection of files, resources
What & how of actual searching15
Tefko Saracevic
• Each has strengths, weaknesses– no ‘perfect’ method
exists• and probably never will
Matchingsearching – system side
• Process of comparing– search: what documents in
the file match the query as stated?
• Various search algorithms:– exact match - Boolean
• still available in most, if not all systems
– best match - ranking by relevance• increasingly used e.g. on the web
– hybrids incorporating both• e.g. Target, Rank in Dialog
Involves many types of search interactions & formulations16
Tefko Saracevic
• Various order of output:– sorted by Last In First Out
(LIFO)– ranked by relevance & then
LIFO– ranked by other
characteristics
• Various forms of output– In Dialog: Output options– in Scopus title (default),
abstract + references, cited by, plus more
• When citations only available: possible links to document delivery– Scopus View at publisher– accessing RUL for digital
journals
• Base for relevance, utility evaluation by users
Retrieved objectsfrom system to user
What a user (or you) sees, gets, judges – can be specified
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Tefko Saracevic
Exact match & best match searches2. Matching algorithms
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Tefko Saracevic
Exact match - Boolean search
• You retrieve exactly what you ask for in the query:– all documents that have the term(s) with logical
connection(s), and possible other restrictions (e.g. to be in titles) as stated in the query
– exactly: nothing less, nothing more
• Based on matching following rules of Boolean algebra, or algebra of sets– ‘new algebra’– presented by circles in Venn diagrams
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Tefko Saracevic
Boolean algebra:operates on sets of documents
• Has four operations (like in algebra):
1. A: retrieves set that has term A • I want documents that
have the term library
2. A AND B: retrieves set that has terms A and B• often called intersection
& labeled A B• I want documents that
have both terms library and digital someplace within
3. A OR B: retrieves set that has either term A or B• often called union and
labeled A B• I want documents that have
either term library or term digital someplace within
4. A NOT B: retrieves set that has term A but not B• often called negation and
labeled A – B• I want documents that have
term library but if they also have term digital I do not want those
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Tefko Saracevic
Potential problems• But beware:
– digital AND library will retrieve documents that have digital library (together as a phrase) but also documents that have digital in the first paragraph and library in the third section, 5 pages later, and it does not deal with digital libraries at all
• thus in Scopus or Google you will ask for “digital library” and in Dialog for digital(w)library to retrieve the exact phrase digital library
– digital NOT library will retrieve documents that have digital and suppress those that along with digital also have library, but sometimes those suppressed may very well be relevant. Thus, NOT is also known as the “dangerous operator “
– also beware of order: venetian AND blind will retrieve documents that have venetian blind and also that have blind venetian (oldest joke in information retrieval)
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Tefko Saracevic
Boolean algebra depicted in Venn diagrams
Four basic operations:e.g. A = digital B= libraries
1 2 3
A BA alone. All documents that have A. Shade 1 & 2. digital
1 2 3
A BA AND B. Shade 2digital AND libraries
1 2 3
A BA OR B. Shade 1, 2, 3digital OR libraries
1 2 3
A BA NOT B. Shade 1digital NOT libraries
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Tefko Saracevic
Venn diagrams … cont.
Complex statements allowed e.g
4
12 35 6
7
A B
C
(A OR B) AND CShade 4,5,6(digital OR libraries) AND Rutgers
(A OR B) NOT CShade what?(digital OR libraries) NOT Rutgers
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Tefko Saracevic
Venn diagrams cont.
• Complex statements can be made– as in ordinary algebra e.g. (2+3)x4
• As in ordinary algebra: watch for parenthesis:– 2+(3 x 4)
is not the same as (2+3)x4
– (A AND B) OR C is not the same as A AND (B OR C)
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Tefko Saracevic
• digital AND libraries can be specified to appear in given fields as present in the given system– e.g. to appear in titles only
• in Dialog command is s digital AND libraries/TI• in Scopus pull down menu allows for selection of given field, – so
for digital library specify Article Title in pull down menu• in Google Advanced Search gets you to a pull down menu for
Where your keywords show up: & then go to in the title of the page
• Various systems have different ways to retrieve singular and plurals for the same term
• in Scopus term library will retrieve also libraries & vice versa• in Dialog you have to specify librar? to retrieve variants• in Google library retrieves library but not libraries
Adding variations to Boolean searches
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Tefko Saracevic
Best match searching• Output is ranked
– it is NOT presented as a Boolean set but in some rank order
• You retrieve documents ranked by how similar (close) they are to a query (as calculated by the system)– similarity assumed as relevance– ranked from highest to lowest relevance to the query
• mind you, as considered by the system• you change the query, system changes rank
– thus, documents as answers are presented from those that are most likely relevant downwards to less & less likely relevant as determined by a given algortihm
– remember: a system algorithm determines relevance ranking
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Tefko Saracevic
Best match ... cont.
• Best match process deals with PROBABILITY:• what is the probability that a document is relevant to a query?
– compares the set of query terms with the sets of terms in documents– calculates a similarity between query & each document based on common
terms &/or other aspects– sorts the documents in order of similarity– assumes that the higher ranked documents have a higher probability of
being relevant– allows for cut-off at a chosen number e.g. the first 20 documents
• BIG issue: What representation & similarity measures are better? Subject of IR experiments– “better” determined by a number of criteria, e.g. relevance, speed …
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Tefko Saracevic
Best match (cont.)• Variety of algorithms (formulas) used to determine
similarity– using statistic &/or linguistic properties
• e.g. if digital appears a lot of times in a given document relative to its size, that document will be ranked higher when the query is digital
– many proposed & tested in IR research– many developed by commercial organizations
• Google also uses calculations as to number of links to/from a document & other methods
• many algorithms are now proprietary & not disclosed– the way a system ranks and you rank may not necessarily be in
agreement • Web outputs are mostly ranked
– but Dialog allows ranking as well, with special commands
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Tefko Saracevic
Best vs. exact matchTraditional IR model
3. Strengths & weaknesses
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Tefko Saracevic
Boolean vs. best match• Boolean
– allows for logic– provides all that has been
matchedBUT– has no particular order of
output – usually LIFO– treats all retrievals equally -
from the most to least relevant ones
– often requires examination of large outputs
• Best match– allows for free terminology– provides for a ranked output– provides for cut-off - any
size outputBUT– does not include logic– ranking method (algorithm)
not transparent• whose relevance?
– where to cut off?
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Tefko Saracevic
Strengths of traditional IR model
• Lists major components in both system & user branches
• Suggests:– What to explain to users about system, if needed– What to ask of users for more effective searching
(problem ...)
• Aids in selection of component(s) for concentration– mostly ever better representation
• Provides a framework for evaluation of (static) aspects
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Tefko Saracevic
Weaknesses• Does not address nor account for interaction &
judgment of results by users– identifies interaction with matching only– interaction is a much richer process
• Many types of & variables in interaction not reflected
• Feedback has many types & functions - also not shown
• Evaluation thus one-sidedIR is a highly interactive process - thus additional model(s)
needed
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Tefko Saracevic
Models. Implications: what happens in searching?
4. IR interaction
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Tefko Saracevic
There is MUCH more to searching than knowing computers, networks & commands, as there is more to writing than knowing word processing packages
Enters interaction
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Tefko Saracevic
• If we consider USER & USE central, then:Interaction is a dominant feature of contemporary IR
• Interaction has many facets:– with systems, technology – with documents, texts viewed/retrieved– intermediaries with people
• Several interactive IR models– none as widely accepted as traditional IR model
• Broader area: human-computer interaction (HCI) studies
IR as interaction
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Tefko Saracevic
HCI: broader concepts“Any interaction takes place through one or more
interfaces & involves two or more participants who each have one or more purposes for the interaction”
Storrs, 1994
• Participants: people & ‘computer’ (everything in it – software, hardware, resources …)
• Interface: a common boundary• Purposes: people have purposes and ‘computer’ has
purposes built in• At issue: identification of important aspects, roles of
each
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Tefko Saracevic
HCI … definitions
“Interaction is the exchange of information between participants where each has the purpose of using the exchange to change the state of itself or of one or more of others”
“An interaction is a dialogue for the purpose of modifying the state of one or more participants”
• Key concepts: exchange, change– for user: change the state of knowledge related to a given
problem, tasks, situation
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Tefko Saracevic
IR interaction is ...
“... the interactive communication processes that occur during the retrieval of information by involving all the major participants in IR, i.e. the user, the intermediary, and the IR system.” Ingwersen, 1992
• Involved:– users– intermediaries (possibly)– everything in IR system– communication processes - exchange of
information
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Tefko Saracevic
Questions
• What variables are involved in interaction?– models give lists
• How do they affect the process? How to control?– experiments, experience, observation give answers
• Do given interventions (actions) or communications improve or degrade the process?– e.g. searcher’s (intermediaries or end-users) actions
• Can systems be designed so that searcher’s intervention improves performance?
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Tefko Saracevic
Interactive IR models
• Several models proposed– none as widely accepted as the traditional IR model
• They all try to incorporate– information objects (“texts”):– IR system & setting– interface– intermediary, if present– user’s characteristics
• cognitive aspects; task; problem; interest; goal; preferences ...
– social environment– variety of processes between them all.
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Tefko Saracevic
User modeling(treated in unit 11, but introduced here to illustrate one of the
important aspect of human-human interaction)
• Identifying elements about a user that impact interaction, searching, types of retrieval …:– who is the user (e.g. education)– what is the problem, task at hand– what is the need; question– how much s/he knows about it– what will be used for– how much wanted, how fast– what environment is involved
• Much more than just analyzing a question posed by user– related to reference interview
• Used to select resources, specify search concepts and terms, formulate query, select format and amount of results provided, follow up with feedback and reiteration, change tactics …
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Tefko Saracevic
• Three differing models are presented here, each concentrates on a different thing:– Ingwersen concentrates on enumeration of general
elements that enter in interaction– Belkin on different processes that are involved as
interaction progresses through time– Saracevic on strata or levels of interaction elements on
computer and user side
• As mentioned, no one interaction model is widely accepted as the traditional IR model
Three interactive models
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Tefko Saracevic
Ingwersen’s interactive cognitive model
• Among the first to view IR differently from traditional model
• Included IR as a system but concentrates also on elements outside system that interact– inf. objects – documents, images …– intermediary – you - & interface– user cognitive aspects– user & general environment– path of request (we call question)
• from environment (problem) to query– path of cognitive changes– path of communication– various other paths of interactions
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Tefko Saracevic
Ingwersen’s model graphically
space
-
Information objects
Interface/Intermediary
Query
User’s cognitive
Request
Environ ment
IR system setting
Cognitive transformations
Interactive
communication
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Tefko Saracevic
Belkin’s episodes model• Concentrates on what happen in interaction as process
– Ingwerson concentrated on elements
• Viewed interaction as a series of episodes where a number of different things happen over time– depending on user’s goals, tasks
• there is judgment, use, interpretation…
– processes of navigation, comparison, summarization …– involving different aspects of information & inf. objects
• While interacting we do diverse things, perform various tasks, & involve different objects
Think: what do you do while searching?Think: what do you do while searching?
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Tefko Saracevic
Belkin’s episodes model
NA
USER
CO
NA
USER
CO
VISUALIZATION
INTERACTIONJudgment, use,interpretation,modification
NAVIGATION
REPRESENTATION
INFOR-MATIONType,mediummodelevel
USERGoalstasks .....
SUMMARIZATION
COMPARISON
Time
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Tefko Saracevic
Saracevic’ stratified model
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• Interaction: considers it as a sequence of processes/episodes occurring in several levels or strata* Interaction = INTREPLAY between levels
• Structure:– Several User levels– Produce a Query – it has characteristics– Several Computer levels– They all meet on the Surface level – Dialogue enabled by Interface
• user utterances• computer ‘utterances’
• Adaptation/changes in all• Geared toward Information use
Tefko Saracevic
Saracevic’s stratification model
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Situationaltasks; work context...
A
dap
tati
on
Engineeringhardware; connections...
A
dap
tati
on
INTE
RA
CTI
ON
S
TRA
TA (l
evel
s)
Surface level
Use
of
info
rmat
ion
Querycharacteristics …
CO
MP
UT
ER
Affectiveintent; motivation ...
Cognitiveknowledge; structure...
Processingsoftware; algorithms …
Contentinf. objects; representations...
US
ER
INTERFACE
Contextsocial, cultural …
Tefko Saracevic
• Defining of what’s involved– whassup?
• Help in recognition/separation of differing variables – each strata or level involves different elements, roles, &
processes
• Observation of interaction between strata - complex dynamics
• On the user side suggests what affects factors query and judgment of responses– thus elements for user modeling
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Roles of levels or strata
Tefko Saracevic
• Interplay on user side:– Cognitive: between cognitive structures of texts & users– Affective: between intentions & other– Situational: between texts & tasks
• Similar interplay on computer side• Surface level - interface:
– searching, navigation, browsing, display, visualization, query characterization …
• Interplay judgments in searching:– evaluation of results - relevance – changing of models: situation, need ...– selection of search terms– resulting modifications - feedback
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Interplay between levels
Tefko Saracevic
Intermediaries - YOU• Intermediaries could participate as an additional
interface - many roles:– diagnostic help in problem, query formulation– system interface handling– selection, interpretation & manipulation of inf.
resources– interpretation of results– education of users– enablers of end-users
• Basic role: optimizing results• Act in processes at different levels
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Tefko Saracevic
Implications
• Interaction central to IR including in searching of the Web• We see it on the surface level
– But result of MANY variables, levels & their interplay
• IR interaction requires knowledge of these levels & interplays– many users have difficulties– so do many professionals
• Design of interfaces for interaction still lacking• People compensate in many ways including trial & error,
failures
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Tefko Saracevic
What happens in searching?
• Highly reiterative process– back & forth between user modeling & (re)formulating
search strategy– goes on & on in many feedback loops, twists & turns,
shifts
• Search strategy (the big picture)– selection/reselection of sources– stating a query (search statement) from a question
• terms, their expansions, logic, qualifications, limitations
Tefko Saracevic
Searching … (cont.)
• Search tactics (action steps) – what to do first, next– e.g. from broad to narrow searches– format of results
• Evaluation of results– as to magnitude - how much? – as to relevance - how well? – feedback to change after that
• user model - e.g. question • strategy - e.g. files, query• tactics - e.g. narrowing, broadening
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Tefko Saracevic
Practical suggestions for searchers (filched from a source I cannot find anymore)
• Prepare carefully• Understand your opponent -
– e.g. Dialog, Scopus, LexisNexis• Anticipate
– e.g. hidden meaning of terms• Have a contingency plan
– assessing odds of success or points of diminishing returns• Avoid ambiguity
– inherent in language• Stay loose!
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Tefko Saracevic
Stay loose?
• I copied that, but always wandered what does it really mean?
• Dictionary says:not firmly fastened or fixed in place
• ???? well, sounds OK!• or
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Tefko Saracevic 57
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