The task definition of the Social Book Search Lab describes complex goal-oriented and non-goal tasks. To satisfy the resulting information needs, the user can utilise and combine different sources of evidence, like, for instance, metadata (e.g. abstract, title, author) and reviews and ratings provided by the user. The challenge is to support the user in this endeavour to create an effective search experience. To this end, in this talk I will discuss how this challenge relates to the well-known principle of polyrepresentation. I will then introduce a probabilistic logic-based framework called POLAR, which is capable of handling complex queries based on the graph induced by user-generated content. Subsequently I will provide a brief outlook on further formal models that try to support the user beyond the typical query-and-result paradigm. The first one is based on quantum probabilities, neatly combining geometry and probability theory to support different forms of user interaction and polyrepresentation. The latter one combines polyrepresentation with probabilistic clustering and the idea of a simulated user.

1. Polyrepresentation in Complex (Book) Search
Tasks
How can we use what the others said?
Ingo Frommholz
University of Bedfordshire
ingo.frommholz@beds.ac.uk
Twitter: @iFromm
CLEF 2015 Social Book Search Workshop
September 10, 2015
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Outline
Motivation
Abstraction for Complex Search Tasks – POLAR
Quantum-inspired Information Access
Polyrepresentation and Clustering
Conclusion

3. Motivation
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Motivating Example: Book Store
“Good introduction to quantum mechanics”

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Motivating Example: Book Store
“Good introduction to quantum mechanics”

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IN Facets and Polyrepresentation
“Good introduction to quantum mechanics”
▶ Relevance decision goes beyond topicality
▶ Collections like Amazon/LT/BritishLibrary
▶ Rich pool of potentially useful information (metadata,
user-generated content)
▶ Different views on documents, relevant for different aspects of the
information need (IN)
▶ Combine the evidence (e.g. metadata and user-generated
content) to get a more accurate estimation of
relevance/usefulness
▶ [Koolen, 2014] puts user-generated content into the index – it
worked!
▶ Reviews and tags complimentary to each other and to
professional metadata
▶ Polyrepresentation a key principle (exploits different
contexts [Ingwersen and Järvelin, 2005])

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Polyrepresentation
Book Store Scenario
Content Author
Ratings
Comments

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Another Challenge

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Some Approaches
▶ POLAR – abstraction layer for complex search tasks utilising
annotations
▶ Quantum Information Access – modelling polyrepresentation
and user interaction
▶ Polyrepresentative clustering – supporting different access
modes (browsing)

10. Abstraction for Complex Search Tasks –
POLAR
. . . . . . . . . . . . . . . . . . . .

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Abstraction for Information Retrieval
▶ Provide a task-oriented solution for knowledge engineers
▶ Should not have to bother with the underlying retrieval model/data
sources/data storage and organization
▶ Instead focus on the task at hand
▶ Support complex retrieval strategies and information needs
▶ Allows for exploiting task-crossovers and synergies as well as
reusing concepts defined for similar tasks

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Annotation Model: Classes, Properties
[Frommholz and Fuhr, 2006b, Agosti et al., 2004]

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POLAR Motivation
Utilising structured annotation hypertexts
▶ Indexing and modelling of structured annotation hypertexts
▶ Querying structured annotation hypertexts
▶ Annotation-based document and discussion search
▶ Support different types of (complex) information needs

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POLAR
Probabilistic Object-Oriented Logics for Annotation-based
Retrieval
[Frommholz and Fuhr, 2006a]
▶ Object-oriented
▶ Classes, instances and relations (attributes), aggregation
▶ Logics
▶ Four-valued logics (true, false, inconsistent, unknown)
▶ Probabilistic
▶ Probabilistic inference and evaluation of rules
▶ Annotation-based retrieval
▶ Models and utilises structured annotation hypertexts
▶ Possible world semantics

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Structured Documents and Content Level Annotations
▶ d[ p *a] : d annotated by content annotation a
▶ p access probability

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Meta Level Annotations
▶ d[ p @j] : d annotated by meta annotation j

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Positive and Negative Annotations
▶ d[ p -*a] : a is negative content annotation
▶ d[ p -@a] : a is negative meta annotation

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Fragments
▶ A fragment f of a document d that is annotated (highlighted) by a:
d[ p1 f|| ... p2 *a|| ]

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References
a[ p =>o] : a references an object o

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Attributes and Classifications
▶ Attributes: Turner is the author of a1:
a1.author(turner)
▶ Classifications: Tweety is a bird, but Roger Rabbit isn’t:
bird(tweety)
!bird(roger_rabbit)

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Database and Structure-oriented Queries
Factual (database-like) queries to the knowledge base. Example:
▶ All annotations written by “turner”:
?- A.author(turner) & annotation(A)
Structure-oriented queries to the knowledge base. Examples:
▶ All content level annotations annotating d1:
?- d1[ *A ]
▶ All documents annotated by a1
?- D[ *a1 ] & document(D)

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Content-oriented Queries
▶ All documents about ‘information’ and ‘retrieval’ which are good
introductions:
?- document(D) & D[ information & retrieval & @A] &
A[ good & introduction ]
▶ All documents having a highlighted part about ‘information’ and
‘retrieval’:
retrieve(D) :- document(D) & D[ ||F ] &
F|| information & retrieval ||
?- retrieve(D)

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No Augmentation

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With Knowledge Augmentation

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Example Application: Ratings
d1[ 0.7 databases
0.5 @a1 0.5 @a2 ]
d2[ 0.8 databases
0.5 @a3 0.5 @a4 ]
a1[ 0.8 excellent ]
a2[ 0.8 excellent ]
a3[ 0.4 excellent ]
a4[ 0.2 excellent ]
excellent_paper(D) :- D[@A] & A[excellent]
?- D[databases] & excellent_paper(D)
0.49 (d1)
0.224 (d2)
databases
databases
0.2 0.4 0.8 1 excellent
d1
d2
a3
a4
a1
a2

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Annotation-based Trustworthiness/Belief
0.7 football
a1[ 0.7 football 0.5 -*a3 0.5 -*a4 ]
a2[ 0.5 football 0.5 *a5 0.5 *a6 ]
topical_relevant(O) :- O[football]
0.6 unconditional_trust(a1)
0.6 unconditional_trust(a2)
trustworthy(O) :- unconditional_trust(O)
trustworthy(O) :- O[*A] /* positive evidence */
!trustworthy(O) :- O[-*A] /* negative evidence */
relevant(O) :- topical_relevant(O) & trustworthy(O)
?- relevant(O)
0.315 (a2)
0.0735 (a1)

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Implementation: POLAR Execution/Translation Pipe
▶ Abstraction layer on top of
Four-valued Probabilistic
Datalog (FVPD)
▶ Implemented in Java
▶ POLAR programs translated
into FVPD
▶ Uses HySpirit as FVPD
implementation
▶ POLAR programs executed
by HySpirit
POLAR
FVPD
PDatalog
PRA
HySpirit

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Implementation: POLAR → FVPD
POLAR
d1[ 0.6 soccer
0.8 s1[ 0.3 music ]
0.7 *a1]
a1[ 0.5 football ]
document(d1)
annotation(a1)
rel(D) :- D[*A] & A[football]
?- rel(D)
FVPD
0.6 term(soccer,d1).
0.8 acc_subpart(d1,s1).
0.7 acc_canno(d1,a1).
0.3 term(music,s1).
0.5 term(football,a1).
instance_of(d1,document,db).
instance_of(a1,annotation,db).
instance_of(D,rel,db):-
acc_canno(D,A) &
term(football,A)
?- instance_of(D,rel,db).

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Implementation: FVPD → pDatalog
FVPD
0.6 term(soccer,d1).
0.8 acc_subpart(d1,s1).
0.7 acc_canno(d1,a1).
0.3 term(music,s1).
0.5 term(football,a1).
instance_of(d1,document,db).
instance_of(a1,annotation,db).
instance_of(D,rel,db):-
acc_canno(D,A) &
term(football,A)
?- instance_of(D,rel,db).
pDatalog
0.6 term4(t,soccer,d1)
0.8 acc_subpart4(t,d1,s1)
0.7 acc_canno4(t,d1,a1)
0.3 term4(t,music,s1)
0.5 term4(t,football,a1)
instance_of4(t,d1,document,db)
instance_of4(t,a1,annotation,db)
pos_instance_of(D,’rel’,’db’) :-
pos_acc_canno(D,A) &
!neg_acc_canno(D,A) &
pos_term(football,A) &
!neg_term(football,A)
?- pos_instance_of(D,rel,db) &
!neg_instance_of(D,rel,db)

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Implementation: pDatalog → PRA
pDatalog
0.6 term4(t,soccer,d1)
0.8 acc_subpart4(t,d1,s1)
0.7 acc_canno4(t,d1,a1)
0.3 term4(t,music,s1)
0.5 term4(t,football,a1)
instance_of4(t,d1,document,db)
instance_of4(t,a1,annotation,db)
pos_instance_of(D,’rel’,’db’) :-
pos_acc_canno(D,A) &
!neg_acc_canno(D,A) &
pos_term(football,A) &
!neg_term(football,A)
?- pos_instance_of(D,rel,db) &
!neg_instance_of(D,rel,db)
PRA
0.6 term4(t,soccer,d1)
0.8 acc_subpart4(t,d1,s1)
...
pos_instance_of =
UNITE(pos_instance_of,
PROJECT[$1,rel,db]
(JOIN[$2=$1](
SUBTRACT(PROJECT[$1,$2]
(JOIN[$2=$2](pos_acc_canno,
SELECT[$1=football](pos_term))),
neg_acc_canno),
...
?- PROJECT[$1](SUBTRACT(
PROJECT[$1](...

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POLAR Evaluation
▶ Evaluation of knowledge augmentation approach...can
annotations improve retrieval effectiveness?
▶ Discussion search
▶ Annotation view on email messages (W3C discussions)
▶ Knowledge augmentation with annotation targets, fragments and
direct annotations
▶ Document search
▶ Using annotations as document context (ZDNet)
▶ Knowledge augmentation (full and radius-1) with annotations
(comments)
▶ Significant improvements observed, but some combinations led
to significantly worse results

32. Quantum-inspired Information Access
. . . . . . . . . . . . . . . . . . . .

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Quantum-inspired Information Access
Quantum Probabilities [van Rijsbergen, 2004, Piwowarski et al., 2010a]
R
p1
p2
p4
p3
p5
▶ System uncertain about user’s
IN
▶ Expressed by an ensemble S of
possible IN vectors :
S = {(p1,|φ1 ⟩),...,(pn,|φn ⟩)}
▶ Probability of relevance:
Pr(R|d,S) = ∑
i
pi ·Pr(R|d,φi )
=||R|φ ⟩||2

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User Interaction and Feedback
R∗
|ϕ1
|ϕ2
|ϕ5
|ϕ3
▶ Outcome of feedback: Query,
relevant document, ...
▶ Expressed as subspace
▶ Project IN vectors onto
document subspace

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User Interaction and Feedback
R∗
|ϕ1
|ϕ2
|ϕ4
|ϕ3
|ϕ5
▶ Outcome of feedback: Query,
relevant document, ...
▶ Expressed as subspace
▶ Project IN vectors onto
document subspace
▶ Document now gets
probability 1
▶ System’s uncertainty
decreases
▶ Also reflects changes in
information needs

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Polyrepresentation/Multiple Evidence
[Frommholz et al., 2010]
Content Author
Ratings
Comments
▶ Polyrepresentation space as tensor product of single spaces
▶ Probability that document is in total cognitive overlap:
Prpolyrep = Prcontent ×Prratings ×Prauthor ×Prcomments
▶ User interaction may lead us into an entangled state (so far
unexplored relationship between polyrepresentation and
entanglement)

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Properties of the Framework
▶ Each user interaction triggers an observation and thus a change
of state
▶ Our evaluation shows that the framework can compete with
standard models in ad hoc IR tasks
▶ Different IR tasks can be formulated in this framework
(filtering [Piwowarski et al., 2010b], query
sessions [Frommholz et al., 2011],
summarisation [Piwowarski et al., 2012])

38. Polyrepresentation and Clustering
. . . . . . . . . . . . . . . . . . . .

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Polyrepresentation and Clustering
▶ Polyrepresentation creates
partitions
▶ Clustering partitions
document sets too
▶ Can clustering help in
creating polyrepresentative
partitions?
▶ Polyrepresentation Cluster
Hypothesis: “documents
relevant to the same
representations should
appear in the same clus-
ter” [Frommholz and Abbasi, 2014].

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Polyrepresentation and Clustering
▶ Mapping of clusters to
polyrepresentation (using
iSearch [Lykke et al., 2010])
▶ Simulated user – search
strategy:
1. User investigates total
cognitive overlap cluster
2. User jumps to different
cluster based on
preferences
3. The user simulation
creates a ranked list of
documents

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Information Need-based Vector
▶ Let REPin be the set of representations1
of an information need in
▶ Motivated by the Optimum Clustering Framework (OCF), which is
based on the probability of relevance [Fuhr et al., 2011]
▶ Pr(R|d,ri ) is computed for each document d and ri ∈ REPin
⃗τin(d) =
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
Pr(R|d,r1)
...
Pr(R|d,rn)

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 (1)
1
search terms, work task, ideal answer, current info need, background knowledge

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Document-based Polyrepresentation Vector
▶ REPd consists of the different representations2
rdi of a document
d
▶ Pr(R|rdi ,q) for q (search terms in this case) is computed
⃗τdoc(d) =
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Pr(R|rd1,q)
...
Pr(R|rdn,q)


 (2)
2
title, abstract, body, bibliographic context, references

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Some Findings (using iSearch)
▶ Some statistically significant improvements over a BM25 baseline
(NDCG@30) using the ranking created by a simple simulated
user strategy when concatenating the IN and Document
representations [Abbasi and Frommholz, 2015b]
▶ Statistical significant improvements (NDCG) when using
document and IN representations separately and assuming an
ideal (oracle-based) cluster ranking
[Abbasi and Frommholz, 2015a]
▶ This shows us our idea is basically promising!
▶ Finding the total cognitve overlap (TOC) using cluster ranking is
challenging [Frommholz and Abbasi, 2014]
▶ Different interpretations of the TOC: The one with the highest
precision? The one with the highest pairwise precision? The one
where all representations get a high value?
▶ The latter one could be identified more easily (MRR = 0.575
compared to around 0.3 for the others)

44. Conclusion
. . . . . . . . . . . . . . . . . . . .

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Conclusion
▶ The rich source of evidence in SBS should be combined to tackle
complex information needs
▶ Probabilistic models for expressing complex information needs
and interactive search
▶ POLAR (abstraction for annotation-based search)
▶ Quantum Information Access
▶ Probabilistic polyrepresentative clustering (simulated user)
▶ It seems polyrepresentation can successfully be applied
▶ Good idea to integrate different sources
▶ Need to do it wisely

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Thanks for your attention!
Questions?

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Frommholz, I. and Fuhr, N. (2006b).
Probabilistic, Object-oriented Logics for Annotation-based
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Frommholz, I., Larsen, B., Piwowarski, B., Lalmas, M., Ingwersen,
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Supporting Polyrepresentation in a Quantum-inspired Geometrical
Retrieval Framework.
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Frommholz, I., Piwowarski, B., Lalmas, M., and van Rijsbergen, K.
(2011).
Processing Queries in Session in a Quantum-Inspired IR
Framework.
In Clough, P., Foley, C., Gurrin, C., Jones, G. J. F., Kraaij, W., Lee,
H., and Mudoch, V., editors, Proceedings ECIR 2011, volume
6611 of Lecture Notes in Computer Science, pages 751–754.
Springer.
Fuhr, N., Lechtenfeld, M., Stein, B., and Gollub, T. (2011).
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Ingwersen, P. and Järvelin, K. (2005).
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Bibliography V
Koolen, M. (2014).
"User Reviews in the Search Index? That’ll Never Work!".
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Lykke, M., Larsen, B., Lund, H., and Ingwersen, P. (2010).
Developing a Test Collection for the Evaluation of Integrated
Search.
In Proceedings ECIR 2010, pages 627–630.
Piwowarski, B., Amini, M.-R., and Lalmas, M. (2012).
On using a Quantum Physics formalism for Multi-document
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Bibliography VI
Piwowarski, B., Frommholz, I., Lalmas, M., and Van Rijsbergen, K.
(2010a).
What can Quantum Theory Bring to Information Retrieval?
In Proc. 19th International Conference on Information and
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Piwowarski, B., Frommholz, I., Moshfeghi, Y., Lalmas, M., and van
Rijsbergen, K. (2010b).
Filtering documents with subspaces.
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van Rijsbergen, C. J. (2004).
The Geometry of Information Retrieval.
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