Transcript Data mining, interactive semantic structuring, and

Data mining, interactive semantic
structuring, and collaboration:
A diversity-aware method
for sense-making in search
Mathias Verbeke,
Bettina Berendt,
Siegfried Nijssen
Dept. Computer Science, KU Leuven
Agenda
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Motivation
Diversity  Diversity-aware tools  (our) Context

Main part
Measures of diversity  Tool
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Outlook
Motivation (1): Diversity is ...
Speaking different
languages (etc.) 
localisation /
internationalisation
 Having different
abilities 
accessibility
 Liking different
things 
collaborative filtering
 Structuring the world
in different ways  ?

Motivation (2):
Diversity-aware applications ...
Must have a (formal) notion of diversity
 Can follow a

– “personalization approach“
 adapt to the user‘s value on the diversity
variable(s)
 transparently? Is this paternalistic?
– “customization approach“
 show the space of diversity
 allow choice / semi-automatic!
(Our) Context
Diversity and Web usage: language, culture
2. Family of tools focussing on interactive sensemaking helped by data mining
1.
– PORPOISE: global and local analysis of news and
blogs + their relations
– STORIES: finding + visualisation of “stories” in news
– CiteseerCluster: literature search + sense-making
– Damilicious: CiteseerCluster + re-use/transfer of
semantics + diversity
Measuring grouping diversity
Diversity = 1 – similarity = 1 - Normalized mutual information
By colour &
NMI = 0
NMI = 0.35
Measuring user diversity
“How similarly do two users group documents?“
 For each query q, consider their groupings gr:
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
For various queries: aggregate
... and now: the application domain
... that‘s only the 1st step!
Workflow
1.
2.
3.
4.
Query
Automatic clustering
Manual regrouping
Re-use
1. Learn + present way(s) of grouping
2. Transfer the constructed concepts
Concepts

Extension
– the instances in a group

Intension
– Ideally: “squares vs. circles“
– Pragmatically: defined via a
classifier
Step 1: Retrieve
CiteseerX via OAI
 Output: set of

– document IDs,
– document details
– their texts
Step 2: Cluster
“the classic bibliometric solution“
 CiteseerCluster:

– Similarity measure: co-citation, bibliometric
coupling, word or LSA similarity, combinations
– Clustering algorithm: k-means, hierarchical
Damilicious: phrases  Lingo
 How to choose the “best“?

– Experiments: Lingo better than k-means at
reconstruction and extension-over-time
Step 3 (a): Re-organise
& work on document groups
Step 3 (b):
Visualising document groups
Steps 4+5: Re-use

Basic idea:
1. learn a classifier from the final grouping (Lingo phrases)
2. apply the classifier to a new search result
 “re-use semantics“

Whose grouping?
– One‘s own
– Somebody else‘s

Which search result?
–
–
–
–
“ the same“ (same query, structuring by somebody else)
“ More of the same“ (same query, later time  more doc.s)
“ related“ (... Measured how? ...)
arbitrary
Visualising user diversity (1)
Simulated users with different strategies
 U0: did not change anything
(“System“)
 U1: tried produce a better fit of the
document groups to the cluster
intensions; 5 regroupings
 U2: attempted to move everything
that did not fit well into the remainder
group “Other topics”, & better fit; 10
regroupings
 U3: attempted to move everything
from „Other topics“ into matching real
groups; 5 regroupings
 U4: regrouping by author and
institution; 5 regroupings
 5*5 matrix of diversities gdiv(A,B,q)
 multidimensional scaling
Visualising user diversity (2)
Web mining
Data mining
RFID
aggregated
using gdiv(A,B)
Evaluating the application

Clustering only: Does it generate
meaningful document groups?
– yes (tradition in bibliometrics) – but: data?
– Small expert evaluation of CiteseerCluster
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Clustering & regrouping
– End-user experiment with CiteseerCluster
– 5-person formative user study of Damilicious
Summary and
(some) open questions
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Damilicious: a tool that helps users in sense-making, exploring
diversity, and re-using semantics
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diversity measures when queries and result sets are different?
how to best present of diversity?
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– How to integrate into an environment supporting user and community
contexts (e.g., Niederée et al. 2005)?
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Incentives to use the functionalities?
how to find the best balance between similarity and diversity?
which measures of grouping diversity are most meaningful?
– Extensional?
– Intensional? Structure-based? Hybrid? (cf. ontology matching)

which other sources of user diversity?
Thanks!