polyrepresentation in complex (book) search tasks - how can we use what the others said?
TRANSCRIPT
Polyrepresentation in Complex (Book) SearchTasks
How can we use what the others said?
Ingo Frommholz
University of [email protected]
Twitter: @iFromm
CLEF 2015 Social Book Search WorkshopSeptember 10, 2015
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Outline
Motivation
Abstraction for Complex Search Tasks – POLAR
Quantum-inspired Information Access
Polyrepresentation and Clustering
Conclusion
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 theinformation need (IN)
▶ Combine the evidence (e.g. metadata and user-generatedcontent) to get a more accurate estimation ofrelevance/usefulness
▶ [Koolen, 2014] puts user-generated content into the index – itworked!
▶ Reviews and tags complimentary to each other and toprofessional metadata
▶ Polyrepresentation a key principle (exploits differentcontexts [Ingwersen and Järvelin, 2005])
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PolyrepresentationBook Store Scenario
Content Author
RatingsComments
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Another Challenge
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Some Approaches
▶ POLAR – abstraction layer for complex search tasks utilisingannotations
▶ Quantum Information Access – modelling polyrepresentationand user interaction
▶ Polyrepresentative clustering – supporting different accessmodes (browsing)
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 asreusing 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 MotivationUtilising 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-basedRetrieval[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 goodintroductions:?- 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 databases0.5 @a1 0.5 @a2 ]
d2[ 0.8 databases0.5 @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
a3a4
a1
a2
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Annotation-based Trustworthiness/Belief
0.7 footballa1[ 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 ofFour-valued ProbabilisticDatalog (FVPD)
▶ Implemented in Java
▶ POLAR programs translatedinto FVPD
▶ Uses HySpirit as FVPDimplementation
▶ POLAR programs executedby HySpirit
POLAR
FVPD
PDatalog
PRA
HySpirit
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Implementation: POLAR → FVPD
POLAR
d1[ 0.6 soccer0.8 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 ledto significantly worse results
Quantum-inspired Information Access
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Quantum-inspired Information AccessQuantum Probabilities [van Rijsbergen, 2004, Piwowarski et al., 2010a]
R
p1
p2
p4
p3
p5
▶ System uncertain about user’sIN
▶ Expressed by an ensemble S ofpossible 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 ontodocument subspace
▶ Document now getsprobability 1
▶ System’s uncertaintydecreases
▶ Also reflects changes ininformation needs
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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 ontodocument subspace
▶ Document now getsprobability 1
▶ System’s uncertaintydecreases
▶ Also reflects changes ininformation needs
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Polyrepresentation/Multiple Evidence[Frommholz et al., 2010]
Content Author
RatingsComments
▶ 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 farunexplored relationship between polyrepresentation andentanglement)
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Properties of the Framework
▶ Each user interaction triggers an observation and thus a changeof state
▶ Our evaluation shows that the framework can compete withstandard models in ad hoc IR tasks
▶ Different IR tasks can be formulated in this framework(filtering [Piwowarski et al., 2010b], querysessions [Frommholz et al., 2011],summarisation [Piwowarski et al., 2012])
Polyrepresentation and Clustering
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Polyrepresentation and Clustering
▶ Polyrepresentation createspartitions
▶ Clustering partitionsdocument sets too
▶ Can clustering help increating polyrepresentativepartitions?
▶ Polyrepresentation ClusterHypothesis: “documentsrelevant to the samerepresentations shouldappear in the same clus-ter” [Frommholz and Abbasi, 2014].
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Polyrepresentation and Clustering
▶ Mapping of clusters topolyrepresentation (usingiSearch [Lykke et al., 2010])
▶ Simulated user – searchstrategy:
1. User investigates totalcognitive overlap cluster
2. User jumps to differentcluster based onpreferences
3. The user simulationcreates a ranked list ofdocuments
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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 isbased on the probability of relevance [Fuhr et al., 2011]
▶ Pr(R|d , ri) is computed for each document d and ri ∈ REPin
τ⃗in(d) =
Pr(R|d , r1)...
Pr(R|d , rn)
(1)
1search 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 documentd
▶ Pr(R|rdi ,q) for q (search terms in this case) is computed
τ⃗doc(d) =
Pr(R|rd1,q)...
Pr(R|rdn,q)
(2)
2title, 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 simulateduser strategy when concatenating the IN and Documentrepresentations [Abbasi and Frommholz, 2015b]
▶ Statistical significant improvements (NDCG) when usingdocument and IN representations separately and assuming anideal (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 ischallenging [Frommholz and Abbasi, 2014]
▶ Different interpretations of the TOC: The one with the highestprecision? The one with the highest pairwise precision? The onewhere all representations get a high value?
▶ The latter one could be identified more easily (MRR = 0.575compared to around 0.3 for the others)
Conclusion
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Conclusion
▶ The rich source of evidence in SBS should be combined to tacklecomplex information needs
▶ Probabilistic models for expressing complex information needsand 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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Bibliography I
Abbasi, M. K. and Frommholz, I. (2015a).Cluster-based Polyrepresentation as Science Modelling Approachfor Information Retrieval.Scientometrics, 102(3):2301–2322.
Abbasi, M. K. and Frommholz, I. (2015b).Polyrepresentative Clustering: A Study of Simulated UserStrategies and Representations.In Mayr, P., Frommholz, I., and Mutschke, P., editors, Proc. of the2nd Workshop on Bibliometric-enhanced Information Retrieval(BIR2015), pages 47–54, Vienna, Austria. CEUR-WS.org.
Agosti, M., Ferro, N., Frommholz, I., and Thiel, U. (2004).Annotations in Digital Libraries and Collaboratories – Facets,Models and Usage.In Heery, R. and Lyon, L., editors, Research and AdvancedTechnology for Digital Libraries. Proc. European Conference on
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Bibliography IIDigital Libraries (ECDL 2004), Lecture Notes in ComputerScience, pages 244–255, Heidelberg et al. Springer.
Frommholz, I. and Abbasi, M. K. (2014).On Clustering and Polyrepresentation.In de Rijke, M., Kenter, T., de Vries, A. P., Zhai, C., de Jong, F.,Radinsky, K., and Hofmann, K., editors, Proceedings of theEuropean Conference on Information Retrieval (ECIR 2014),volume 1, pages 618–623. Springer.
Frommholz, I. and Fuhr, N. (2006a).Evaluation of Relevance and Knowledge Augmentation inDiscussion Search.In Gonzalo, J., Thanos, C., Verdejo, M. F., and Carrasco, R. C.,editors, Research and Advanced Technology for Digital Libraries.Proc. of the 10th European Conference on Digital Libraries (ECDL2006), Lecture Notes in Computer Science, pages 279–290,Heidelberg et al. Springer.
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Bibliography III
Frommholz, I. and Fuhr, N. (2006b).Probabilistic, Object-oriented Logics for Annotation-basedRetrieval in Digital Libraries.In Nelson, M., Marshall, C., and Marchionini, G., editors, Proc. ofthe 6th ACM/IEEE Joint Conference on Digital Libraries (JCDL2006), pages 55–64, New York. ACM.
Frommholz, I., Larsen, B., Piwowarski, B., Lalmas, M., Ingwersen,P., and van Rijsbergen, K. (2010).Supporting Polyrepresentation in a Quantum-inspired GeometricalRetrieval Framework.In Proceedings of the 2010 Information Interaction in ContextSymposium, pages 115–124, New Brunswick. ACM.
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Bibliography IV
Frommholz, I., Piwowarski, B., Lalmas, M., and van Rijsbergen, K.(2011).Processing Queries in Session in a Quantum-Inspired IRFramework.In Clough, P., Foley, C., Gurrin, C., Jones, G. J. F., Kraaij, W., Lee,H., and Mudoch, V., editors, Proceedings ECIR 2011, volume6611 of Lecture Notes in Computer Science, pages 751–754.Springer.
Fuhr, N., Lechtenfeld, M., Stein, B., and Gollub, T. (2011).The Optimum Clustering Framework : Implementing the ClusterHypothesis.Information Retrieval, 14.
Ingwersen, P. and Järvelin, K. (2005).The turn: integration of information seeking and retrieval incontext.Springer-Verlag New York, Inc., Secaucus, NJ, USA.
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Bibliography V
Koolen, M. (2014)."User Reviews in the Search Index? That’ll Never Work!".In Proceedings ECIR 2014, pages 323–334.
Lykke, M., Larsen, B., Lund, H., and Ingwersen, P. (2010).Developing a Test Collection for the Evaluation of IntegratedSearch.In Proceedings ECIR 2010, pages 627–630.
Piwowarski, B., Amini, M.-R., and Lalmas, M. (2012).On using a Quantum Physics formalism for Multi-documentSummarisation.Journal of the American Society for Information Science andTechnology (JASIST).
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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 andKnowledge Management, pages 59–68.
Piwowarski, B., Frommholz, I., Moshfeghi, Y., Lalmas, M., and vanRijsbergen, K. (2010b).Filtering documents with subspaces.In Proceedings of the 32nd European Conference on InformationRetrieval (ECIR 2010), pages 615–618.
van Rijsbergen, C. J. (2004).The Geometry of Information Retrieval.Cambridge University Press, New York, NY, USA.