Information Retrieval in
Context of Digital Libraries
- or DL in Context of IR
Peter Ingwersen
Royal School of LIS
Denmark – [email protected]
http://www.db.d/pi
Agenda

Information Retrieval
 In
Context of Information Behavior
 Laboratory Model = Digital Library approach?
 Integrated Model – roles of context
 The
social perspective
Challenges in IR / DL according to model
 Conclusions

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Information Retrieval
 The
processes involved in the
representation, storage, searching,
finding, filtering, presentation and
use of information relevant to a
requirement for information
desired by a human user (The Turn, 2005)
 Interaction – Time dimension
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Information behaviour and IR
T. Wilson´s Onion Model, 1999 - extended:
Job-related
Work Tasks
Interests
Non-job-related
Tasks and Interests
Daily-life behavior
Seeking
Interactive
IR
IR
Information behaviour
Behaviour
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Information behaviour … and other
central concepts in Information Studies

Information behaviour:
 to
create information – e.g., on the Net blogs; also human indexing, including
social tagging;
 to produce publications – e.g., as publisher
 to communicate – face-to-face; chat; email
 to manage information sources – e.g. KM;
selectivity
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IB and other central concepts …

Information seeking (behaviour)




Information behaviour with interest for Information
Information need exist – even muddled or
exploratory
Searching information sources – e.g. colleagues
Information Retrieval (I)IR


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Searching information space via systems – Digital
Library & Assets (interactive IR)
Retrieval models; relevance feedback & ranking;
query modification; auto indexing and weighting;
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The Laboratory Model of IR
(in the Cranfield-TREC Laboratory Research Framework)
Documents
Search
request
Representation
Representation
Database
Query
Matching
Query
Result
Pseudo
Relevance
Feedback
Could just as well be a model for Digital Library development
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The Lab IR Cave, with a Visitor
The Turn – Ingwersen & Järvelin, 2005
Context
Documents
Search
request
Representation
Representation
Database
Query
Matching
Query
Result
Simplistic model of (I)IR – short-term
interaction – in context
Information
objects
Social Tagging
Org.
R = Request / Relevance feedback
Query
Modification
IT: Engines
Logics
Algorithms
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Interface
R
Social
Social
Information
Seeker(s) Interaction Context
Recommender techniques
Cultural
Short-term IS&R & social interaction
Cognitive transformations and influence over time
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Central Components of Interactive IR –
the basic Integrated Framework
In situ tagging
Information
objects
Org.
Interface
Cognitive
Actor(s)
Social
Context
(team)
IT: Engines
Logics
Algorithms
The Lab./DL
Framework
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Cultural
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In situ
recommendation
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Integrated Framework and Relevance Criteria
Socio-organizational& cultural context
Work task context
Seeking context
Docs
Request
Repr
Repr
DB
Seeking
Task
Work Task
Seeking
Process
Work
Process
Seeking
Result
Task
Result
Query
Match
Result
IR context
D: Socio-cognitive relevance; quality
Evaluation
Criteria:
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of work task result
C: Quality work process/result; Graded R.
B: Usability, Graded rel., CumGain; Quality of information/process
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A: Recall, precision, efficiency
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Moving into Context

Strength:
 Involvement
of TASK (work/search) and …
 Processes for fulfillment of task and …
 Task result / outcome
Seeking and retrieval tasks influenced by
work tasks
 Pointing to novel relevance measures

 Task
fulfillment measures; socio-cognitive
relevance; social utility (tagging, visits,
downloads …)
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Challenges to IR/DL

“[If] we consider that unlike art IR is not there
for its own sake … then IR is far, far more than
a branch of computer science”

And what information and relevance means to IR, Tefko Saracevic
states (1997, p. 17) …

“[In] broadest sense: Information is … that
involves not only messages (first sense) that are
cognitively processed (second sense), but also a
context – a situation, task, problem-at-hand, the
social horizon, … intentions …”
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Challenges to IR/DL – 2

Understanding actors’ goals, tasks
intentions – in diversity of contexts
 Job-related
knowledge enquiries
 Daily-life information explorative behaviors
 Entertainment - or simply ‘meaning making’

Inference of goals, tasks, intentions from
implicit evidence from interaction behavior
 Implicit
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relevance feedback study examples
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Challenges to IR/DL – 3
Leading to finding out the best algorithmic
models and solutions – not in themselves
– but given understanding of
characteristics of searcher goals, …
 A lot of searching is undirected, vague,
random, exploratory, muddled … (Skov,
2009)
 A lot of tagging (and folksonomies) is
randomly done - but can be filtered

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Challenges to IR/DL – 4
Belkin, Nick. Sigir Forum, 42(1), 2008: 47-54

Recommender systems and personalization are
relying on a narrow conception, applying vague
correlations between a current searcher’s
situation and previous




Dwell time on page;
Click-through
Viewed, rated or saved objects by other searchers
Search profiles’ contents
To tailor the rank of search results
 Or to find ‘things alike’ (probably better)

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Challenges to IR/DL – 5
Which of the (personal) contextual features do
we need to involve – incl. the IT context?
 How to present retrieved and filtered
documents?




Zooming in/out – integrated searching of media &
document types: presentation form and
relevance/usability:
Are interface issues solved by Google snippets and
Microsoft’s detail-whole format?
Alternative (elaborated) evaluation methods for
interaction design (IR/DL) are required
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The Circle of Systemic/Social Contexts in
interaction design: Digital Libraries & (I)IR –
actor as centre
Info.
Objects
IR
Interactio
n
Interface
Org.
Cognitive
Actor(s)
Social
Interactio
n
Social
Context
(team)
IT
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Cultural
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Conclusions

IR and DL (or Digital Assets including
museums and cultural heritage) face same
challenges of addressing the
 Interactive
process
nature of the information
 Contexts
– and their limits
 Evaluation & research approaches
 Need for combined efforts of IT and behavior
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Thank You!
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