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Gheorghe Muresan

SCILS, Rutgers University

Document Clustering for

Mediated Information Access

– The WebCluster Project –

Gheorghe Muresan

School of Communication, Information and Library SciencesRutgers University

The original WebCluster project was conducted at the Robert Gordon University, Aberdeen, UK.It was supervised by Prof. David J. Harper and sponsored by Ubilab, Zurich.

Current work is being conducted in collaboration with Ph.D. student Hyuk-Jin Lee andProf. Nicholas J. Belkin.

Exploratory Search Interfaces: Categorization, Clustering and BeyondWorkshop at HCIL 2005, University of Maryland, June 2, 2004

Gheorghe Muresan


WebCluster - Motivation

InformationNeed

Query Search engine

(within some subject domain)

WWW_SearchEngine

Domain

Ø Gulfs

– information need ↔ query

– structured subject domain ↔ unstructured target collection (WWW)

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Information

need1. Select library

2. Consult catalog

3. Browse

shelves

4. Use inter-library scheme

Information Need

Formulation

Interaction in the library

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1. Select source

collection

Information Need

Formulation

2. Explore

source collection

with ClusterBookResults

Results

Information

need

3. Search WWW

Can we simulate the library interaction ?

Structured

source

collections

Gheorghe Muresan


The mediated access interaction

Information

need

Web

sea

rch

en

gin

e

Web

Clu

ster

Query

Specialised

source

Target collection

(WWW)

Topical

documents

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Interaction model vs. prototype

Ø Structuring the source collectionwDocument clustering

w Supervised classification

wManual (intellectual) classification

Ø Exploring the structured source collectionwMetaphor – Library, book, encyclopaedia

w Visualization tool – Folder metaphor, hyperbolic tree, themescape, cone trees, thematic maps

w Search strategies supported – Best match or cluster-based searching, browsing

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Model vs. prototype

Ø Interaction model

w Explicit (the user marks relevant documents) vs. implicit (cues

on relevance are derived based on user behavior/actions)

w Transparent (the user is aware) vs. opaque (the user is happy

to see effect of ‘magic’)

w Automatic vs. manual/intellectual generation of the mediated query

Ø Query model

w Language models (generative, Kullback-Leibler)

w Probabilistic models

w Rocchio or other RF-specific formulae

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ClusterBook - Source collection

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ClusterBook - Target collection

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Informal experiments

- Objectives -

Ø Test the users’ reaction to the mediated access concept

Ø Test the user satisfaction regarding the functionality of the system, and the relevance of the documents retrieved

Ø Formative usability testing - some volunteers were not only experienced searchers, but also had experience in evaluating IR systems

Ø Comparison of user generated queries vs. system generated queries

Ø Note. These experiments were run at different stages of the development

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- Experimental procedure -

Ø Subjects received introduction to the system

Ø Task assigned: “You are a trainee in a newspaper. You support the journalists by providing information for the topic of their articles.”

Ø Sample topics:

w The history of the Brasilian debt crisis

w How are the quotas for growing coffee set and controlled on a world-wide basis ?

Ø Source collection: a sub-collection of Reuters (newspaper articles)

Ø Steps followed by users (explicit scenario):w Formulate a query and record it

w Browse source collection, select ‘best’ cluster, edit query generated by system, submit it to the search engine

w Submit to the same search engine the initial, self-generated query

w Compare results of the two searches

Gheorghe Muresan



- Results -

Ø Users found the mediation useful for unfamiliar topics

Ø The system nearly always proposed new, good query terms

Ø Users not always good at recognizing ‘good’ query terms

Ø The system proposed bad query terms (not specific to the topic)

⇒ the opaque scenario not viable unless the query formulation is improved

Ø The two-step process was questioned when:

w the query formulation was considered easy, for a familiar topic

w the documents of the source collection were considered sufficient to cover the information need

Ø Complete link, group average – OK; single link – bad

Ø Overall, the system is usable

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Consequences of informal experiments

Ø Formal experiments are needed to verify the main assumptions:

w The Cluster Hypothesis holds for a specialized collection

w Good clusters can be found with the search strategies provided

w Mediated queries can improve retrieval effectiveness

Ø The effect on retrieval performance of various parameters should be compared

w Weighting schemes

w Clustering methods

w Search strategies

Gheorghe Muresan


Fixed Plants

Coastal Wind Farms

Pacific Rim

Wind FarmsDesign of Coastal

Wind Farms

Design of

….

Desert

Wind Farms

Inland Wind Farms

...

Portable Generators

...

Wind generators

for yachts

Power Generation Propulsion

Wind Energy

Critical issue: The label generationw Document representatives

w searching

w Cluster representatives

w browsing

w searching

w mediation

w Collection representatives

w collection selection

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Mediation experiment - simulationsØ Objectives:

w Test the potential of mediation to increase retrieval effectiveness

w Test the effect on performance of a variety of parameters

Search engine

Search engine

Simple query generator

(baseline)

Topic-based mediator

(upperbound)

Sourcecollection

Targetcollection

Cluster-based mediation

(realistic mediation)

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Experimental setup

Ø Interactive track of TREC-8

w Offers relevance judgments for complex topics, with a multitude of aspects

w Offers the experimental design for the user experimentw Six topics with 12 to 56 aspects each

w Target collection: FT 1991-4, with 210,158 articles

w Source collection built based on relevance judgments: half of the relevant documents, their nearest neighbors, plus the documents judged non-relevant

Gheorghe Muresan


Results – the cluster

hypothesis

Ø Aspectual cluster hypothesis confirmed by an extended

version of the van Rijsbergen –

Sparck Jones separation test

w Similarity between pairs of docs covering the same aspect is higher than between pairs of docs covering the same topics, which is higher than between pairs of docs in the collection

Ø Consequence confirmed: clustering groups documents in

pockets of relevance

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Results – retrieval effectiveness

Ø Tf-Idf > KL > RelFreq as weighting schemes for document representation

Ø Adding disambiguation terms to the query increases recall, but decreases precision

Ø Nearest-neighbor mediation (“more like this”) highly significantly improves both recall and precision, even if just one exemplary document is offered for each topic aspect

Ø Cosine and Dice performs similarly

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Mediation results

Ø Upperbound experiment (all relevant docs

known in source)

w Both recall and precision increase with query length

w Query term weights strongly affect performance

w No evidence that uniformity of term frequency affects

performance

Ø Clustered source mediation

w Best cluster mediation increases P, decreases R

w “Fuse and search” – strong increase in R and P

w “Search and fuse” – good R, terrible P !

Gheorghe Muresan


User experiment – effectiveness of mediated

information retrieval for Web searches

Source & target –based mediation

On the fly clustering

Structured

(cluster)

Source-based

mediationBaseline

Linear

(list)

MediatedUnaided

Query formulation

(between subjects)

Re

su

lt p

res

en

tati

on

(wit

hin

su

bje

cts

)

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User experiment – no mediation

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User experiment – mediated access

Gheorghe Muresan


Contributions of WebCluster

Ø Proposes and explores system-based mediated access to very large heterogeneous document collections

Ø Explores the use of clustering for capturing the topical, semantic structure of a problem domain (as represented by a specialized collection)

Ø Explores the use of language models for building cluster and document representatives

Ø Offers a framework for building structured portals on the WWW

Ø Offers a framework for building collaborative environments

Gheorghe Muresan


WebCluster - Other applications

Ø CD-ROM based collections

w structured access to the collection itself

w mediated access to WWW (via CD-ROM)

Ø Mediated access (portals) via hierarchically structured information sources

Examples are: via large structured report (e.g. government reports), via structured collection of information (e.g. encyclopaedia), via intranet

Ø Multimedia information access

w cluster multimedia source, e.g. annotated photographs

w mediated access to other photographs (not annotated)

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Other directions for WebCluster

Ø Clustered vs. categorized source collection

Ø Language model – based labels vs. specialized

terminology based on a domain ontology /

thesaurus

Ø Different interaction and visualization metaphors

w Spring-embedded algorithms for 2D representation of

clusters

Ø Various inter-document similarities (faceted ?)

Ø User profiles / personalization

w Change of interest over time

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Topic representation

Ø What (weighted) terms best describe a topic ?

w Applications:

w Clustering – generating cluster representatives

w Mediation – generating mediated queries

w Machine generation

w Simulation based on test topics and relevance judgments

w Use various weighting formulae and cut-off points

w Which representations are more effective ? What do they have in common / specific ?

w Human (manual / intellectual generation)

w Compare queries generated by searchers in TREC tasks

– Effectiveness

– Keyword vs. natural language queries

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Questions ?

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Query formulation problemsØ Vague information need

Ø Vocabulary mismatch

Ø Difficulty of query language syntax

Ø Lack of context, ambiguity of terms

Ø Lack of a search strategy

Ø No understanding of the underlying indexing/searching model

Ø Note. TREC experiments have shown that the quality of the query has a higher impact on retrieval effectiveness than weighting schemes or search algorithms.

Gheorghe Muresan


Role of structure

Computing

Computer

Screen Keyboard C++Pascal

Programming language

...

Mathematics

...

Algebra

Computing, Mathematics Physics

Science

Ø Reveals the semantic structure of the domain & its concepts

Ø Groups (semantically ?) similar documents

Ø Supports exploration and concept formation

Ø Supports term disambiguation (context)

Ø (Has potential for efficient retrieval)

Ø (Has potential for effective retrieval)

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Browsing label

(relative cluster representative)

Coastal Wind Farms Inland Wind Farms

Pacific

Rim Wind

Farms

Design of Coastal

Wind Farms

Design of

….

Desert

Wind Farms

Wind generators

for yachts

Fixed Plants

...

Portable Generators

...Power Generation Propulsion

Wind Energyparenti

clusteri

clusteriiip

ppparentclusterKLR

,

,

, log),( ==

Gheorghe Muresan


Searching label

(absolute cluster representative)

Coastal Wind Farms Inland Wind Farms

Pacific

Rim Wind

Farms

Design of Coastal

Wind Farms

Design of

….

Desert

Wind Farms

Wind generators

for yachts

Fixed Plants

...

Portable Generators

...Power Generation Propulsion

Wind Energycollectioni

clusteri

clusteriiip

ppcollectionclusterKLA

,

,

, log),( ==

Gheorghe Muresan


Mediation label

(Expanded cluster representative)

Fixed Plants

Coastal Wind Farms

Pacific

Rim Wind

Farms

Design of Coastal

Wind Farms

Design of

….

Desert

Wind Farms

Inland Wind Farms

...

Portable Generators

...

Wind generators

for yachts

Power Generation Propulsion

Wind Energyri

r

ri

r

iiii

AA

AAAE

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Gheorghe Muresan


Topic model representations

Exemplary representation

Statistical representation

Statistical analysis

Language model

Context analysis

Typical terms, weighted

Thresholding

Mediated query

Keyword representation

Gheorghe Muresan


The cluster hypothesisØ Reminder: the original cluster hypothesis

w “Closely associated documents tend to be relevant to the same requests” (van Rijsbergen)

Ø Aspectual cluster hypothesis: Highly similar documents tend to be relevant to the same topic. However, documents relevant to the same topic may be quite dissimilar if they cover distinct aspects of the topic.w Consequence: Clustering algorithms tend to group

together documents that cover highly focused topics, or aspects of complex topic. Documents covering distinct aspects of complex topics tend to be spread over the cluster structure.

Gheorghe Muresan


Aspects of relevance in the mediated

access process

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Distribution of relevant documents

in clustersClustering vs. Random

0%

5%

10%

15%

20%

25%

Clusters

Re

ca

ll

Clustering Random

Gheorghe Muresan


WebCluster scenario#1

Document from the source collection

Document from the target collection (WWW)

W

e

b

C

l

u

s

t

e

r

Web

Search

Engine

c0

c4 c5

c2c1 c3

c’0

c’3

c’2

c’5

WWWØ Name

w Transparent mediated access

Ø Targeted users

w Experienced searchers

Ø Specific

w The users are aware of the mediation process, of the separation between the source and target collections

w The users have the option to edit the query generated (proposed) by the system. They understand the indexing / searching model.

Gheorghe Muresan


WebCluster scenario#2

W

e

b

C

l

u

s

t

e

r

c0

c4 c5

c2c1 c3

WWW

c’0

c’3

c’2

c’5

Web

Search

Engine

Ø Name

w Opaque mediated access

Ø Targeted users

w Naive / beginner searchers

Ø Specific

w The users explore the structure of the domain, which contains sample documents, and have the option of asking for similar documents

w The users are unaware of the mediation process - the query generation and target search are not visible

Document from the source collection

Document from the target collection (WWW)

Gheorghe Muresan


Initial user interface (Java AWT)