ir traditional model

© Tefko Saracevic 1

Information retrieval (IR):

traditional model

1. Why? Rationale for the module. Definition of IR

2. System & user components

3. Exact match & best match searches

4. Strengths & weaknesses


1. Why? Rationale for the module.

Definition of IR

includes problems addressed in IR


Why? • Every online database, every

search engine, everything that is searched online is based in some way or another on principles developed in IR– IR is at the heart of searching used in

systems such as DIALOG, LexisNexis & others

• Understanding the basics of IR is a prerequisite for understanding how searching of online systems works.


You are asking:

• What basic elements and processes are involved in IR?

• What are the conceptual bases for searching?

• How are these applied in practice?


IR: - original definition

“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.”

Calvin Mooers, 1951


IR:Objective & problems

Provide the users with effective access to & interaction with information resources.

Problems addressed:

1. How to organize information intellectually?

2. How to specify search & interaction intellectually?

3. What systems & techniques to use for those processes?

Where do you fit? With what problems do you deal?


2. System & user components

Traditional IR model presented


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 treated in this module

– interactive: more realistic, favored now, shows also interactions involved treated in next module (module 5)

– Each has strengths, weaknesses


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


Traditional IR model

File organizationindexed documents

Acquisitiondocuments, objects

Representationindexing, ...

Probleminformation need

Representationquestion

Querysearch formulation

Matchingsearching

Retrieved objects

feedba

ck

System User


• Content: What is in files, resources– in DIALOG first part of blue sheets: File

Description, Subject Coverage

• Selection of documents & other objects from various sources– in blue sheets: Sources

• Mostly text based documents– full texts, titles, abstracts ...– but also other objects:

data, statistics, images, maps, trade marks, sounds ...

Acquisition(system)

Importance:Determines contents – what

is in it Key to file, resource

selection !!!


• 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

Representationof documents, objects

(system)

Basic to what is available for searching & displaying


• 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

• Large file approaches– for efficient retrieval by computers

File organization(system)

Enables searching & interplay between types of files


• Related to user’s task, situation

– vary in specificity, clarity

• Produces information need– ultimate criterion for effectiveness of

retrievalhow 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 timeyou will experience this in your term project

Problem(user)

Critical for examination in interview


• Non-mediated: end user alone• Mediated: intermediary + user

– interviews; human-human interaction

• Question analysis– selection, elaboration of terms– various tools may be used

thesaurus, classification schemes, dictionaries, textbooks, catalogs …

• Focus toward– deriving search terms & logic– selection of files, resources

• Subject to feedback changes • Critical roles of intermediary - you

Representation - question( user & possibly system)

Determines search specification - a dynamic process


• Translation into systems requirements & limits – start of human-computer interaction

query is the thing that goes into the computer

• Selection of files, resources• Search strategy - selection of:

– search terms & logic– possible fields, delimiters – controlled & uncontrolled vocabulary– variations in effectiveness tactics

• Reiterations from feedback – several feedback types: relevance feedback,

magnitude feedback *...– query expansion & modification

Query - search statement(user & system)

What & how of actual searching


Clarifying difference

• Question is what user asks and what you may then have elaborated

• Query is what is asked of computer to match – what is put in

• Question is transformed into query• Question:

– I am interested in major historical developments in the area of information retrieval?

• Query– history information retrieval (in Google)– history AND information(w)retrieval (in

DIALOG) (plus you have to select which file(s) to search)


• Process of matching, 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 bothe.g. Target, Rank in DIALOG

• Each has strengths, weaknesses– no ‘perfect’ method exists

and probably never will

Matching - searching(user & system)

Involves many types of search interactions & formulations


• Various order of output:– Last In First Out (LIFO); sorted– ranked by relevance– ranked by other characteristics

• Various forms of output– In DIALOG: Output options

• When citations only: possible links to document delivery

• Base for relevance, utility evaluation by users

• Relevance feedback

Retrieved documents(from system to user)

What a user (or you) sees, gets, judges – can be specified


3. Exact match & best match searches

Getting to that Boolean and similar stuff – the nitty-gritty

of matching

which actually affects how you formulate the query


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


Boolean algebra• Operates on sets

– e.g. set of documents

• Has four operations (like in algebra):1. A: retrieve set A

I want documents that have the term library

2. A AND B: retrieve set that has 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: retrieve set that has either 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: retrieve set 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


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 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 “


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 B

A AND B. Shade 2

digital AND libraies

1 2 3

A B

A OR B. Shade 1, 2, 3

digital OR libraries

1 2 3

A B

A NOT B. Shade 1

digital NOT libraries


Venn diagrams … cont.

Complex statements allowed e.g

4

12

3

5 6

7

A B

C

(A OR B) AND C

Shade 4,5,6

(digital OR libraries) AND Rutgers

(A OR B) NOT C

Shade what?

(digital OR libraries) NOT Rutgers


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)


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 systemyou 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

– can be cut at any desired number - e.g. first 10


Best match ... cont.• Best match process deals with

PROBABILITY:– 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

• BIG issue: What representation & similarity measures are better?– “better” determined by a number of criteria,

e.g. relevance, speed …


Best match (cont.)

• Variety of algorithms (formulas) used to determine similarity– using statistic &/or linguistic properties

e.g. if digital appears a lot 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

organizationsGoogle also uses calculations as to number

of links to/from a document many algorithms are now proprietary

– system ranking and your ranking may not necessarily be in agreement

• Web outputs are mostly ranked• But DIALOG allows ranking as well,

with special commands


4. Strengths & weaknesses


Boolean vs. best match

• Boolean– allows for logic– provides all that

has been matched

BUT– has no particular

order of output– 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 output

BUT– does not include

logic– ranking method

(algorithm) not transparent

whose relevance?

– where to cut off?


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 ...)

• Selection of component(s) for concentration– mostly ever better representation

• Provides a framework for evaluation of (static) aspects


Weaknesses

• Does not address nor account for interaction & judgment of results by users– identifies interaction with search 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-sided

IR is a highly interactive process- thus additional model(s) needed


Interactive models

• Explored in next module

Module 5

ir traditional model

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