1

FacetAtlas: Multifaceted
Visualization for Rich Text
CorporaNan Cao, Jimeng Sun, Yu-Ru Lin, David Gotz
Shixia Liu, Huamin Qu
InfoVis 2010

2Introduction

3

multiple facets

4

multiple facets

Symptoms
Treatments
CausesTests & Diagnosis
Prognosis
Prevention
Complications

5

Diabetes

6

Type2
MetabolicSyndrome

Diabetes

Type1

Gestational
Diabetes

7

Type2
MetabolicSyndrome

Diabetes

Type1

Gestational
Diabetes
How to visualize the relationsof multifaceted document contents?

8

Type2
MetabolicSyndrome

Diabetes(Q1) How to model the
document contents into
multifaceted relation
data?
(Q2) How to intuitively
visualize multifaceted
document contents andtheir relations?

Type1

GestationalDiabetes(Q3) How to find theinsight patterns visually
driven by users’interests?

9Solution• Goal :– Visualize both the global (clusters) and local (relations)patterns in rich text corpora with multiple facets.

• Approach :– Multifaceted entity-relational data model– Intuitive visual encoding and automatic layout– Users’ interests driven interaction for pattern detection

10Demo11

Key Challenges(Q1) How to model the document
contents into multifaceted relation
data?
(Q2) How to intuitively visualize multifaceted document contents and theirrelations?
(Q3) How to find the insight patterns visually driven by users’ interests?

(Q1) How to model the document contents
into multifaceted relational data ?
document set

facet
segmentation

entity extraction
type 1diabetesdiseasesymptom

entity set

multifaceted entity relational data modeltype 2diabetes
thirstInternal
relationsblurredvisiontreatment
takemedications

12

blood sugar
control

External
relations

13

Key Challenges(Q1) How to model the document contents into multifaceted
relation data?

(Q2) How to intuitively visualize
multifaceted document contents
and their relations?
(Q3) How to find the insight patterns visually driven by users’interests?

(Q2) How to visualize multifaceted
document contents and their relations?

2

1
1, 2
3, 4

1, 2

<4, 2><4, 3><5, 3><5, 1><1, 2><1, 3>

3, 4
5, 6

data model

2, 3
4

encodinglayout14

(Q2) How to visualize multifaceted
document contents and their relations?

2

1
1, 2
3, 4

1, 2

<4, 2><4, 3><5, 3><5, 1><1, 2><1, 3>

3, 4
5, 6

data model

2, 3
4

encodinglayout15

16

Encoding

Multifaceted Entity
Relational Model16

17

Encoding
1

disease1
2
Type 1Diabetes

342

3
Type 2Diabetes

54
6

Multifaceted Entity
Relational Model17

18

Encoding

External relations
Multifaceted entities

symptoms

treatments

1

disease12
Type 1Diabetes

3
Internal relations

42

3
Type 2Diabetes

54
6

Multifaceted Entity
Relational Model18

19

Encoding

External relations
Group entities byexternal relations

Multifaceted entities

symptoms

treatments

1

disease1
Group internal
relations

2Type 1Diabetes

3
Internal relations

42

3
Type 2Diabetes

54
6

Multifaceted Entity
Relational Model19

20

Encoding

Encoded external
relation between
disease facet andsymptom facet

Facet Nodetreatments
Grouped internal
relation

Overlapped
entities has
multiple replicas

1, 2
3, 4

<1, 5><2, 4><3, 4><3, 5>
3, 4
5, 6

Type 1Diabetes

1. Encode external
relations byneighborhood

1

2. Split overlap
entities into
multiple replicas

2

3. Group relatedentities and theirreplicas in into
the facet node

3
Type 2Diabetes

4

4. Grouping therelated internal
linkages in thesymptom facet

21

Encodingtreatments

Symptomfacet nodedisease1, 2
3, 4<1, 5><2, 4><3, 4><3, 5>
3, 4
5, 6

1, 2
Type 1Diabetes<1, 2><1, 3>
Type 2Diabetes

2, 3
4

1. Similarly groups
the treatments
entities into thetreatment facetnode
2. Then we
encoded thedata model into
visual form

(Q2) How to visualize multifaceted
document contents and their relations?

211, 23, 4

1, 2<4, 2><4, 3><5, 3><5, 1><1, 2><1, 3>

3, 4
5, 6

data model

2, 3
4

encodinglayout22

23

Layout10,000 entities and30,000 external relations

23

24

Layoutsampling

entity layout

density estimation

link layout

24

25

Layoutsampling

entity layout

density estimation

link layout

onlinedocument set

facet
segmentation entity extraction

offlineSampling by DOI

diseasesymptom
treatment

build indicesrelated samples
query

26

Layoutsampling

entity layout

density estimation

Stabilized LayoutBased on the hidden internal relations of primary facet
Keep users’ mental map while data changed
212min   2 X i  X j  d ij   X i  pre( X i )  i j d
i
jij







Cluster Together

More smoothly

link layout

27

LayoutCluster Layout

sampling

entity layout

Kernel Density
RNNEstimation

density estimation

link layout

28

Layoutsampling

entity layout

density estimation

link layout

Link Layout (1)Layout external
relationsswappingrotating

29

Layoutsampling

entity layoutdensity estimation

link layout

Link Layout (2)graph partitionedge bundling30Fever31

Diabetes

32HIV33

HIV

Where are our patterns?
What can we find ?

34

Key Challenges(Q1) How to model the document contents into multifaceted relation data?

(Q2) How to visualize multifaceted information to reveal both global and localpatterns?

(Q3) How to find the insight patterns
visually driven by users’ interests?

(Q3) How to find insights via userinteractions?
Symptom view
Disease view

Keyword QueryContext Switch
FilteringHighlighting
A set of interactionsare designed toaddress users’interests

contextswitch

filtering35

36Visual Patterns

Symptoms of HIV• Global cluster patterns• Local multifaceted relational pattern– Co-occurrences pattern– Outlier pattern

Headache
Fatigue

Fever
Shortness ofBreathCo-occurrence

Outlier37(Q3) Interview of domain experts
What did domain experts (3 physicians) say?
 “enhance the current thought process of physicians, and help
create the subtle associations between different concepts.”
 “this will be very helpful for nurses who run the self-care
education activities to better engage patients.”
 “this tool has great potential as an education tool for interns
and residents who have just started their medical career”
 “extremely creative and has great potential for clinical therapeutic
usage and diagnosis decision support”

38

Summary• Problem : How to visualize relations of multifaceted
document contents ? Global / Local patterns• Approach :• Result :39

FacetAtlas: Multifaceted
Visualization for Rich Text
CorporaNan Cao, Jimeng Sun, Yu-Ru Lin, David Gotz,Shixia Liu, Huamin Qu
InfoVis 201040Related Work
Visualizing Global
Content Patterns

S. Havre, et al.
InfoVis 2000

Tag CloudF. van Ham, et al.
InfoVis 2009

M. W. Christopher, et al.
Vast 2009

F. van Ham, et al.
InfoVis 2009

G. Smith
TVCG 2006

H. Strobelt, et al.
InfoVis 09

Visualizing Local
Relational Patterns

A. Pere, et al.
InfoVis 2006

SearchInterface

Grokker41

Related Work
Visualizing Global
Content Patterns

S. Havre, et al.
InfoVis 2000

Tag CloudH. Strobelt, et al.
InfoVis 09

Our Focus :Extract complex relations
Visualizing Localfrom document contents
Relational Patterns
byF. vanHam,considering
et al.
M. W. Christopher, et al.
Vast 2009
InfoVis 2009
different aspects

A. Pere, et al.
InfoVis 2006

SearchInterfaceF. van Ham, et al.
InfoVis 2009

G. Smith
TVCG 2006

Grokker

42

Evaluations

42

User study

43• Participants– 3 domain experts (2 physicianswith 30 years experience in thehealthcare domain, and 1 young
medical professional)– 20 common users without
medical background (2 groupsand 10 for each)• 6 study tasks based on the Google
Health online documents– T4 : identify the facet with the mostcross-cluster connections.
– T6 : identify the facet with the mostoverall connection across entities.

• Baseline– Enhanced Traditional Graph
Visualization– Based on the same framework withsimilarly interactions on the samedataset43

44

Evaluation Results from non-experts
Complete Timesurveys

Task Success Rate

Result (based on two tail t-test)•
Significant efficiency improvement in–––•

Visualizing the clusters
Showing an overview of multiple
connections across clusters
Representing the details of multifaceted
connection between entities

Slight improvement in–Finding the most connective facet within acluster

(Q3) How to find insights via userinteractions?

What data content
need to befetched?

How to fetch? (sql,lucene query)Interactive UIInteraction
interpretation
Keyword QueryContext Switch
Zooming
FilteringHighlighting
A set of interactions
are designed toaddress users’interests

Query
Generator

ResultProcessor

dataindices

Fetch the data andconvert the result
into visual form

query driven interaction mechanism

46

47

(Q3) How to find visual patterns driven byuser interests
show allsymptoms
relatedwith diabetes

Fetch symptomsof diabetes

Context Switch

disease view
Increasedthirstblur ofvisionInteraction
interpretation

SQL
Select symptom fromtable where disease =“diabetes”

QueryGenerator

ResultProcessor

dataindices

Covert the data into
visual form

symptom view
query driven interaction mechanism

48

Layoutsampling

entity layout

density estimation

Link Layout (2)Rotating step tunes node and linkage
orientations by minimize the global
tension based on a force model r  R min  k f i sin kkikikii

link layout

49

Local PatternsM• Co-occurrence

simij   simk i, j 
k 1– Entities have strong connections overmultiple entities– Semantic similarity metric defines whatis “strong”• Outlier– Entities have “strong ” connections
however “far away” from each other– Layout closeness defines what is “faraway”– “strong ” and “far away”

d ij the shortest path in the graph• Enhancement by colors–

Automatic adjust the saturation of nodecolor by pattern metrics

cij  d ij  simij