Transcript SIMS 247 Information Visualization and Presentation Marti Hearst

SIMS 247
Information Visualization
and Presentation
Marti Hearst
March 15, 2002
Outline
• Why Text is Tough
• Visualizing Concept Spaces
– Clusters
– Category Hierarchies
• Visualizing Query Specifications
• Visualizing Retrieval Results
• Usability Study Meta-Analysis
Why Visualize Text?
• To help with Information Retrieval
– give an overview of a collection
– show user what aspects of their interests are
present in a collection
– help user understand why documents retrieved as
a result of a query
• Text Data Mining
– Mainly clustering & nodes-and-links
• Software Engineering
– not really text, but has some similar properties
Why Text is Tough
• Text is not pre-attentive
• Text consists of abstract concepts
– which are difficult to visualize
• Text represents similar concepts in many
different ways
– space ship, flying saucer, UFO, figment of imagination
• Text has very high dimensionality
– Tens or hundreds of thousands of features
– Many subsets can be combined together
Why Text is Tough
The Dog.
Why Text is Tough
The Dog.
The dog cavorts.
The dog cavorted.
Why Text is Tough
The man.
The man walks.
Why Text is Tough
The man walks the cavorting dog.
So far, we can sort of show this in pictures.
Why Text is Tough
As the man walks the cavorting dog, thoughts
arrive unbidden of the previous spring, so unlike
this one, in which walking was marching and
dogs were baleful sentinals outside unjust halls.
How do we visualize this?
Why Text is Tough
• Abstract concepts are difficult to
visualize
• Combinations of abstract concepts are
even more difficult to visualize
– time
– shades of meaning
– social and psychological concepts
– causal relationships
Why Text is Tough
• Language only hints at meaning
• Most meaning of text lies within our minds
and common understanding
– “How much is that doggy in the window?”
• how much: social system of barter and trade (not the
size of the dog)
• “doggy” implies childlike, plaintive, probably cannot do
the purchasing on their own
• “in the window” implies behind a store window, not
really inside a window, requires notion of window
shopping
Why Text is Tough
• General categories have no standard ordering
(nominal data)
• Categorization of documents by single topics
misses important distinctions
• Consider an article about
– NAFTA
– The effects of NAFTA on truck manufacture
– The effects of NAFTA on productivity of truck
manufacture in the neighboring cities of El Paso
and Juarez
Why Text is Tough
• Other issues about language
– ambiguous (many different meanings for
the same words and phrases)
– different combinations imply different
meanings
Why Text is Easy
• Text is highly redundant
– When you have lots of it
– Pretty much any simple technique can pull out
phrases that seem to characterize a document
• Instant summary:
– Extract the most frequent words from a text
– Remove the most common English words
Guess the Text
478 said
233 god
201 father
187 land
181 jacob
160 son
157 joseph
134 abraham
121 earth
119 man
118 behold
113 years
104 wife
101 name
94 pharaoh
Text Collection Overviews
• How can we show an overview of the
contents of a text collection?
– Show info external to the docs
• e.g., date, author, source, number of inlinks
• does not show what they are about
– Show the meanings or topics in the docs
• a list of titles
• results of clustering words or documents
• organize according to categories (next time)
Clustering for Collection Overviews
– Scatter/Gather
• show main themes as groups of text
summaries
– Scatter Plots
• show docs as points; closeness indicates
nearness in cluster space
• show main themes of docs as visual
clumps or mountains
– Kohonen Feature maps
• show main themes as adjacent polygons
– BEAD
• show main themes as links within a forcedirected placement network
Clustering for Collection Overviews
• Two main steps
– cluster the documents according to the
words they have in common
– map the cluster representation onto a
(interactive) 2D or 3D representation
Text Clustering
• Finds overall similarities among groups
of documents
• Finds overall similarities among groups
of tokens
• Picks out some themes, ignores others
Scatter/Gather
S/G Example: query on “star”
Encyclopedia text
8 symbols
68 film, tv (p)
97 astrophysics
67 astronomy(p)
10 flora/fauna
14 sports
47 film, tv
7 music
12 steller phenomena
49 galaxies, stars
29 constellations
7 miscelleneous
Clustering and re-clustering is entirely automated
Scatter/Gather
Cutting, Pedersen, Tukey & Karger 92, 93, Hearst & Pedersen 95
• How it works
– Cluster sets of documents into general “themes”, like a
table of contents
– Display the contents of the clusters by showing topical
terms and typical titles
– User chooses subsets of the clusters and re-clusters
the documents within
– Resulting new groups have different “themes”
• Originally used to give collection overview
• Evidence suggests more appropriate for
displaying retrieval results in context
• Appearing (sort-of) in commercial systems
Northern Light Web Search: Started
out with clustering. Then integrated
with categories. Now does not do web
search and uses only categories.
Teoma: appears to combine
categories and clusters
(Chen et al. 97)
Scatter Plot of Clusters
BEAD (Chalmers 97)
BEAD (Chalmers 96)
An example layout produced by Bead, seen in overview,
of 831 bibliography entries. The dimensionality
(the number of unique words in the set) is 6925.
A search for ‘cscw or collaborative’ shows the pattern
of occurrences coloured dark blue, mostly to the right.
The central rectangle is the visualizer’s motion control.
Themescapes (Wise et al. 95)
Example: Themescapes
(Wise et al. 95)
Clustering for Collection Overviews
• Since text has tens of thousands of
features
– the mapping to 2D loses a tremendous
amount of information
– only very coarse themes are detected
Galaxy of News
Rennison 95
Galaxy of News
Rennison 95
(594 docs)
(Lin 92, Chen et al. 97)
Kohonen Feature Maps
Study of Kohonen Feature Maps
• H. Chen, A. Houston, R. Sewell, and B.
Schatz, JASIS 49(7)
• Comparison: Kohonen Map and Yahoo
• Task:
– “Window shop” for interesting home page
– Repeat with other interface
• Results:
– Starting with map could repeat in Yahoo (8/11)
– Starting with Yahoo unable to repeat in map
(2/14)
How Useful is Collection Cluster
Visualization for Search?
Three studies find negative results
Study 1
•
Kleiboemer, Lazear, and Pedersen. Tailoring a retrieval system for naive
users. In Proc. of the 5th Annual Symposium on Document Analysis
and Information Retrieval, 1996
• This study compared
– a system with 2D graphical clusters
– a system with 3D graphical clusters
– a system that shows textual clusters
• Novice users
• Only textual clusters were helpful (and they
were difficult to use well)
Study 2: Kohonen Feature Maps
• H. Chen, A. Houston, R. Sewell, and B. Schatz, JASIS 49(7)
• Comparison: Kohonen Map and Yahoo
• Task:
– “Window shop” for interesting home page
– Repeat with other interface
• Results:
– Starting with map could repeat in Yahoo (8/11)
– Starting with Yahoo unable to repeat in map
(2/14)
Study 2 (cont.)
• Participants liked:
– Correspondence of region size to #
documents
– Overview (but also wanted zoom)
– Ease of jumping from one topic to another
– Multiple routes to topics
– Use of category and subcategory labels
Study 2 (cont.)
• Participants wanted:
–
–
–
–
–
–
–
–
–
hierarchical organization
other ordering of concepts (alphabetical)
integration of browsing and search
correspondence of color to meaning
more meaningful labels
labels at same level of abstraction
fit more labels in the given space
combined keyword and category search
multiple category assignment (sports+entertain)
Study 3: NIRVE
•
NIRVE Interface by Cugini et al. 96. Each rectangle is a cluster. Larger clusters closer to
the “pole”. Similar clusters near one another. Opening a cluster causes a projection that
shows the titles.
Study 3
•
Visualization of search results: a comparative evaluation of text, 2D,
and 3D interfaces Sebrechts, Cugini, Laskowski, Vasilakis and Miller,
Proceedings of SIGIR 99, Berkeley, CA, 1999.
• This study compared:
– 3D graphical clusters
– 2D graphical clusters
– textual clusters
• 15 participants, between-subject design
• Tasks
–
–
–
–
–
Locate a particular document
Locate and mark a particular document
Locate a previously marked document
Locate all clusters that discuss some topic
List more frequently represented topics
Study 3
• Results (time to locate targets)
–
–
–
–
Text clusters fastest
2D next
3D last
With practice (6 sessions) 2D neared text results; 3D still
slower
– Computer experts were just as fast with 3D
• Certain tasks equally fast with 2D & text
– Find particular cluster
– Find an already-marked document
• But anything involving text (e.g., find title) much
faster with text.
– Spatial location rotated, so users lost context
• Helpful viz features
– Color coding (helped text too)
– Relative vertical locations
Visualizing Clusters
• Huge 2D maps may be inappropriate
focus for information retrieval
– cannot see what the documents are about
– space is difficult to browse for IR purposes
– (tough to visualize abstract concepts)
• Perhaps more suited for pattern
discovery and gist-like overviews
Co-Citation Analysis
• Has been around since the 50’s.
(Small, Garfield,
White & McCain)
• Used to identify core sets of
– authors, journals, articles for particular fields
– Not for general search
• Main Idea:
– Find pairs of papers that cite third papers
– Look for commonalitieis
• A nice demonstration by Eugene Garfield at:
– http://165.123.33.33/eugene_garfield/papers/mapsciworld.html
Co-citation analysis (From Garfield 98)
Co-citation analysis (From Garfield 98)
Co-citation analysis (From Garfield 98)
Category Combinations
Let’s show categories instead of clusters
DynaCat (Pratt, Hearst, & Fagan 99)
DynaCat (Pratt 97)
• Decide on important question types in
an advance
– What are the adverse effects of drug D?
– What is the prognosis for treatment T?
• Make use of MeSH categories
• Retain only those types of categories
known to be useful for this type of
query.
DynaCat Study
• Design
– Three queries
– 24 cancer patients
– Compared three interfaces
• ranked list, clusters, categories
• Results
– Participants strongly preferred categories
– Participants found more answers using categories
– Participants took same amount of time with all
three interfaces
HiBrowse
Category Combinations
• HiBrowse Problem:
– Search is not integrated with browsing of
categories
– Only see the subset of categories selected
(and the corresponding number of
documents)
MultiTrees
(Furnas & Zacks ’94)
Cat-a-Cone:
Multiple Simultaneous Categories
• Key Ideas:
– Separate documents from category labels
– Show both simultaneously
• Link the two for iterative feedback
• Distinguish between:
– Searching for Documents vs.
– Searching for Categories
Cat-a-Cone Interface
Cat-a-Cone
• Catacomb:
(definition 2b, online Websters)
“A complex set of interrelated things”
• Makes use of earlier PARC work on
3D+animation:
Rooms
IV: Cone Tree
Web Book
Henderson and Card 86
Robertson, Card, Mackinlay 93
Card, Robertson, York 96
browse
search
query terms
Category
Hierarchy
Collection
Retrieved Documents
ConeTree for Category Labels
• Browse/explore category hierarchy
– by search on label names
– by growing/shrinking subtrees
– by spinning subtrees
• Affordances
– learn meaning via ancestors, siblings
– disambiguate meanings
– all cats simultaneously viewable
Virtual Book for Result Sets
– Categories on Page (Retrieved Document)
linked to Categories in Tree
– Flipping through Book Pages causes some
Subtrees to Expand and Contract
– Most Subtrees remain unchanged
– Book can be Stored for later Re-Use
Improvements over Standard Category
Interfaces
Integrate category selection with
viewing of categories
 Show all categories + context
 Show relationship of retrieved
documents to the category structure
 But … do users understand and like
the 3D?

The FLAMENCO Project
• Basic idea similar to Cat-a-Cone
• But use familiar HTML interaction to
achieve similar goals
• Usability results are very strong for
users who care about the collection.
Query Specification
Command-Based Query Specification
• command attribute value connector …
– find pa shneiderman and tw user#
• What are the attribute names?
• What are the command names?
• What are allowable values?
Form-Based Query Specification (Altavista)
Form-Based Query Specification (Melvyl)
Form-based Query Specification (Infoseek)
Menu-based Query Specification
(Young & Shneiderman 93)
Context
Putting Results in Context
• Visualizations of Query Term Distribution
– KWIC, TileBars, SeeSoft
• Visualizing Shared Subsets of Query Terms
– InfoCrystal, VIBE, Lattice Views
• Table of Contents as Context
– Superbook, Cha-Cha, DynaCat
• Organizing Results with Tables
– Envision, SenseMaker
• Using Hyperlinks
– WebCutter
Putting Results in Context
• Interfaces should
– give hints about the roles terms play in the
collection
– give hints about what will happen if various
terms are combined
– show explicitly why documents are
retrieved in response to the query
– summarize compactly the subset of interest
KWIC (Keyword in Context)
• An old standard, ignored until recently by internet
search engines
– used in some intranet engines, e.g., Cha-Cha
Display of Retrieval Results
Goal: minimize time/effort for deciding
which documents to examine in detail
Idea: show the roles of the query terms
in the retrieved documents, making use
of document structure
TileBars
Graphical Representation of Term
Distribution and Overlap
Simultaneously Indicate:
– relative document length
– query term frequencies
– query term distributions
– query term overlap
Example
Query terms:
DBMS (Database Systems)
Reliability
What roles do they play in retrieved documents?
Mainly about both DBMS
& reliability
Mainly about DBMS, discusses
reliability
Mainly about, say, banking, with
a subtopic discussion on
DBMS/Reliability
Mainly about high-tech layoffs
Exploiting Visual Properties
– Variation in gray scale saturation imposes a
universal, perceptual order (Bertin et al. ‘83)
– Varying shades of gray show varying
quantities better than color (Tufte ‘83)
– Differences in shading should align with the
values being presented (Kosslyn et al. ‘83)
Key Aspect: Faceted Queries
• Conjunct of disjuncts
• Each disjunct is a concept
– osteoporosis, bone loss
– prevention, cure
– research, Mayo clinic, study
• User does not have to specify which are main
topics, which are subtopics
• Ranking algorithm gives higher weight to
overlap of topics
– This kind of query works better at highprecision queries than similarity search
(Hearst 95)
TileBars Summary
Preliminary User Studies
users understand them
find them helpful in some situations, but
probably slower than just reading titles
sometimes terms need to be
disambiguated
SeeSoft: Showing Text Content using a linear representation and
brushing and linking (Eick & Wills 95)
Query Term Subsets
Show which subsets of query terms
occur in which subsets of
documents occurs in which subsets
of retrieved documents
Term Occurrences in Results Sets
Show how often each query term
occurs in retrieved documents
– VIBE (Korfhage ‘91)
– InfoCrystal (Spoerri ‘94)
– Problems:
• can’t see overlap of terms within docs
• quantities not represented graphically
• more than 4 terms hard to handle
• no help in selecting terms to begin with
InfoCrystal (Spoerri 94)
VIBE (Olson et al. 93, Korfhage 93)
Term Occurrences in Results Sets
– Problems:
• can’t see overlap of terms within docs
• quantities not represented graphically
• more than 4 terms hard to handle
• no help in selecting terms to begin with
DLITE (Cousins 97)
• Supporting the Information Seeking
Process
– UI to a digital library
• Direct manipulation interface
• Workcenter approach
– experts create workcenters
– lots of tools for one task
– contents persistent
DLITE
(Cousins 97)
• Drag and Drop interface
• Reify queries, sources, retrieval results
• Animation to keep track of activity
Slide by Shankar Raman
IR Infovis Meta-Analysis (Chen & Yu ’00)
• Goal
– Find invariant underlying relations suggested
collectively by empirical findings from many
different studies
• Procedure
– Examine the literature of empirical infoviz studies
• 35 studies between 1991 and 2000
• 27 focused on information retrieval tasks
• But due to wide differences in the conduct of the studies
and the reporting of statistics, could use only 6 studies
IR Infovis Meta-Analysis (Chen & Yu ’00)
• Conclusions:
– IR Infoviz studies not reported in a standard
format
– Individual cognitive differences had the largest
effect
• Especially on accuracy
• Somewhat on efficiency
– Holding cognitive abilities constant, users did
better with simpler visual-spatial interfaces
– The combined effect of visualization is not
statistically significant
– Misc
• Tilebars and Scatter/Gather are well-known enough to
not require citations!!