Faceted Metadata in Search Interfacespeople.ischool.berkeley.edu/~hearst/talks/hearst_aaai05.pdf · Marti Hearst: UC Berkeley SIMS AAAI’05 Invited talk: Faceted Metadata in Search

Faceted Metadata in Search Interfaces

Marti HearstUC Berkeley School of Information

This Research Supported by NSF IIS-9984741.

AAAI’05 Invited talk: Faceted Metadata in Search InterfacesMarti Hearst: UC Berkeley SIMS

Focus: Search and Navigation of Large Collections

ImageCollections

E-GovernmentSites

Example: the University of California Library Catalog

Shopping SitesDigital Libraries





What do we want done differently?

• Organization of results• Hints of where to go next• Flexible ways to move around

• … How to structure the information?


The Problem with Hierarchy


The Problem With Hierarchy


The Problem with Hierarchy


The Problem With Hierarchy

Where is Berkeley?College and University > Colleges and Universities

>United States > U > University of California > Campuses > Berkeley

U.S. States > California > Cities >Berkeley > Education > College and University > Public > UC Berkeley


Outline• Motivation: support for browsing big collections

– Focus on usability for a wide range of lay users• Approach: flexible application of hierarchical

faceted metadata– Advantages of the approach– Results of usability studies

• Opportunities for AI:– Creating faceted category hierarchies– Assigning items to categories– Combine categories to identify tasks– A way to focus for personalization research


Why Care? These folks do:• NYTimes archive• eBay• California Digital Library• US Census


How to Structure Information for Search and Browsing?

• Hierarchy is too rigid

• KL-One is too complex

• Hierarchical faceted metadata:– A useful middle ground


What are facets?• Sets of categories, each of which describe a

different aspect of the objects in the collection.• Each of these can be hierarchical.• (Not necessarily mutually exclusive nor

exhaustive, but often that is a goal.)

Time/Date Topic RoleGeoRegion + + +


Facet example: Recipes

Course

Main Course

CookingMethod

Stir-fry

Cuisine

Thai

Ingredient

Red Bell Pepper

Curry

Chicken


Example of Faceted Metadata:Categories for Biomedical Journal Articles

1. Anatomy [A]2. Organisms [B] 3. Diseases [C] 4. Chemicals and Drugs [D]

1. Lung 2. Mouse3. Cancer 4. Tamoxifen


Goal: assign labels from facets


MotivationDescription: 19th c. paint horse; saddle and hackamore; spurs; bandana on rider; old time cowboy hat; underchin thong; flying off.

NatureAnimal

MammalHorse

OccupationsCowboy

ClothingHats

Cowboy Hat

MediaEngraving

Wood Eng.

LocationNorth America

America


MotivationDescription: 19th c. paint horse; saddle and hackamore; spurs; bandana on rider; old time cowboy hat; underchin thong; flying off.

By using facets,what we are not capturing?

The hat flew off;The bandana stayed on.

The thong is part of the hat.

The bandana is on the cowboy(not the horse). The saddle is on the horse (not the cowboy).


Hierarchical Faceted Metadata

• A simplification of knowledge representation

• Does not represent relationships directly

• BUT can be understood well by many people when browsing rich collections of information.


How to Put In an Interface?Some Challenges:

• Users don’t like new search interfaces.

• How to show lots of information without overwhelming or confusing?


A Solution (The Flamenco Project)• Use proper HCI methods.

• Organize search results according to the faceted metadata so navigation looks similar throughout– Easy to see what to go next, were you’ve been

– Avoids empty result sets

– Integrates seamlessly with keyword search


The Flamenco Project• Incorporating Faceted Hierarchical Metadata into

Interfaces for Large Collections• Key Goals:

– Support integrated browsing and keyword search• Provide an experience of “browsing the shelves”

– Add power and flexibility without introducing confusion or a feeling of “clutter”

– Allow users to take the path most natural to them

• Method:– User-centered design, including needs assessment and

many iterations of design and testing


Art History Images Collection


Questions we are trying to answer

• How many facets are allowable?• Should facets be mixed and matched?• How much is too much?• Should hierarchies be progressively revealed,

tabbed, some combination?• How should free-text search be integrated?

















Information previews• Use the metadata to show where to go next

– More flexible than canned hyperlinks– Less complex than full search

• Help users see and return to previous steps• Reduces mental work

– Recognition over recall– Suggests alternatives

• More clicks are ok iff (J. Spool)• The “scent” of the target does not weaken• If users feel they are going towards, rather than away,

from their target.


What is Tricky About This?• It is easy to do it poorly• It is hard to be not overwhelming

– Most users prefer simplicity unless complexity really makes a difference

– Small details matter• It is hard to “make it flow”


eBay Products




Search Usability Design Goals1. Strive for Consistency2. Provide Shortcuts3. Offer Informative Feedback4. Design for Closure5. Provide Simple Error Handling6. Permit Easy Reversal of Actions7. Support User Control8. Reduce Short-term Memory Load

From Shneiderman, Byrd, & Croft, Clarifying Search, DLIB Magazine, Jan 1997. www.dlib.org


Usability Studies• Usability studies done on 3 collections:

– Recipes: 13,000 items– Architecture Images: 40,000 items– Fine Arts Images: 35,000 items

• Conclusions:– Users like and are successful with the

dynamic faceted hierarchical metadata, especially for browsing tasks

– Very positive results, in contrast with studies on earlier iterations.


Post-Test Comparison

15 16

2 30

1 29

4 28

8 23

6 24

28 3

1 31

2 29

FacetedBaseline

Overall AssessmentMore useful for your tasks

Easiest to useMost flexible

More likely to result in dead endsHelped you learn more

Overall preference

Find images of rosesFind all works from a given period

Find pictures by 2 artists in same media

Which Interface Preferable For:


Advantages of the Approach• Honors many of the most important usability

design goals– User control– Provides context for results– Reduces short term memory load– Allows easy reversal of actions– Provides consistent view

• Allows different people to add content without breaking things

• Can make use of standard technology


Advantages of the Approach• Systematically integrates search results:

– reflect the structure of the info architecture– retain the context of previous interactions

• Gives users control and flexibility – Over order of metadata use– Over when to navigate vs. when to search

• Allows integration with advanced methods– Collaborative filtering, predicting users’ preferences


Disadvantages• Does not model relations explicitly• Does it scale to millions of items?

– Adaptively determine which facets to show for different combinations of items

• Requires faceted metadata!


Opportunities for AI

• Creating hierarchical faceted categories– Assigning items to those categories– Adaptively adding new facets as data changes

• A new approach to personalization: – User-tailored facet combinations

• Create task-based search interfaces– Equate a task with a sequence of facet types


Creating Classifications from Data• Most approaches are associational

– AKA clustering, LSA, LDA, etc.– This leads to poor results when applied to text

• To derive facets, need a different angle– We have a simple approach based on

WordNet


Clustering (The Hope)


Clustering (The Hope)


Clustering (The Reality)


Clustering (The Reality)


Example: Recipes (3500 docs)


Blei, Ng, & Jordan ’03 (Latent Dirichlet Allocation)




Sanderson & Croft ’99Term Subsumption


Sanderson & Croft ’99Term Subsumption


Stoica & Hearst ’04WordNet-based








Example: AP NewswireP-2 ABSTRACT The Bechtel Group Inc. offered in 1985 to sell oil to Israel at a discount of at least $650 million for 10years if it promised not to bomb a proposed Iraqi pipeline, a Foreign Ministry official said Wednesday. But then-Prime Minister Shimon Peres said the offer from Bruce Rappaport, a partner in the San Francisco-based construction and engineering company, was ``unimportant,'' the senior official told The Associated Press. Peres, now foreign minister, never discussed the offer with other government ministers, said the official, who spoke on condition of anonymity. The comments marked the first time Israel has acknowledged any offer was made for assurances not to bomb the planned $1 billion pipeline, which was to have run near Israel's border …















Associational techniques• Pros:

– Sometimes terms grouped to get a general concept• Airline, airplane, pilots, flight

• Cons:– Highly unpredictable– Not comprehensive

• Dollar and yen but no deutchmarks• Eastern but no other directions

– Not uniform in subject matter• Mixing currencies with countries with timing • Mixing compass directions with airlines


Lexical Hierarchy-based• Pros

– Faceted and hierarchical– Consistent is-a hierarchies– Comprehensiveness more likely

• Cons– Doesn’t provide overall themes

• Airlines, pilots, airplanes– Sometimes uses wrong word sense– Sometimes the right term/hierarchy is not present

• Doesn’t have “dish type” nor “cuisine” for recipes• Specialized domains won’t work


Our Approach• Leverage the structure of WordNet

Doc

umen

ts

WordNet

Get hypernym

paths

Sel

ect

term

s

Build tree Compress

tree


Our Approach• Leverage the structure of WordNet


1. Select Terms

red blue

• Select well distributed terms from collection D

ocum

ents

WordNet

Get hypernym

pathsSele

ct te

rms

Build tree

Comp. tree


2. Get Hypernym Path

red blue

chromatic color

abstraction

property

visual property

color

red, redness

abstraction

property

visual property

color

blue, blueness

chromatic color

Doc

umen

ts

WordNet

Get hypernym

pathsSel

ect t

erm

s

Build tree

Comp. tree


3. Build Tree

red blue

chromatic color

abstraction

property

visual property

color

red, redness

abstraction

property

visual property

color

blue, blueness

chromatic color

red blue

abstraction

property

visual property

color

red, redness

chromatic color

blue, blueness

Doc

umen

ts

WordNet

Get hypernym

pathsSel

ect t

erm

s

Buildtree

Comp. tree


4. Compress Tree

Doc

umen

ts

WordNet

Get hypernym

pathsSel

ect t

erm

s

Build tree

Comp.tree

red, redness

color

red

chromatic color

blue, blueness

blue

green, greenness

greengreenred

color

chromatic color

blue


4. Compress Tree (cont.)

red

color

chromatic color

blue green

color

red blue green

Doc

umen

ts

WordNet

Get hypernym

pathsSel

ect t

erm

s

Build tree

Comp. tree


Disambiguation• Ambiguity in:

– Word senses– Paths up the hypernym tree

Sense 1 for word “tuna”organism, being

=> plant, flora=> vascular plant

=> succulent=> cactus

=> tuna

Sense 2 for word “tuna”organism, being

=> fish=> food fish

=> tuna=> bony fish

=> spiny-finned fish=> percoid fish

=> tuna

2 paths for same word

2 paths for

same sense


How to Select the Right Senses and Paths?

• First: build core tree– (1) Create paths for words with only one sense– (2) Use Domains

• Wordnet has 212 Domains– medicine, mathematics, biology, chemistry, linguistics, soccer, etc.

• Automatically scan the collection to see which domains apply• The user selects which of the suggested domains to use or

may add own • Paths for terms that match the selected domains are added to

the core tree

• Then: add remaining terms to the core tree.


Using Domains

dip glosses:

Sense 1: A depression in an otherwise level surface

Sense 2: The angle that a magnet needle makes with horizon

Sense 3: Tasty mixture into which bite-size foods are dipped

dip hypernyms

Sense 1 Sense 2 Sense 3

solid shape, form food

=> concave shape => space => ingredient, fixings

=> depression => angle => flavorer

Given domain “food”, choose sense 3


Opportunities for AI• New opportunity: Tagging, folksonomies

– (flickr de.lici.ous)– People are created facets in a decentralized manner– They are assigning multiple facets to items– This is done on a massive scale– This leads naturally to meaningful associations



http://www.airtightinteractive.com/projects/related_tag_browser/app/






This Doesn’t Solve Everything• Harder to determine what’s related to more

complex terms• Still not good for finding a recipe using potatoes





Linking Metadata Into Tasks

• Old Yahoo restaurant guide combined:– Region – Topic (restaurants) – Related Information

• Other attributes (cuisines)• Other topics related in place and time (movies)


Green: restaurants & attributes

Red: related in place & time

Yellow: geographic region


Other Possible Combinations• Region + A&E• City + Restaurant + Movies• City + Weather• City + Education: Schools• Restaurants + Schools• …


Creating Tasks from HFM• Recipes Example:

– Click Ingredient > Avocado– Click Dish > Salad– Implies task of “I want to make a Dish type d with an

Ingredient i that I have lying around”– Maybe users will prefer to select tasks like these over

navigating through the metadata.


Summary

• Flexible application of hierarchical faceted metadata is a proven approach for navigating large information collections.– Midway in complexity between simple hierarchies and

deep knowledge representation.• Perhaps HFM is a good stepping stone to deeper

semantic relations

– Currently in use on e-commerce sites; spreading to other domains


AI Opportunities• Creating hierarchical faceted categories

– Assigning items to those categories– Adaptively adding new facets as data

changes• A new approach to personalization:

– User-tailored facet combinations• Create task-based search interfaces

– Equate a task with a sequence of facet types• Make use of folksonomies data!


Acknowledgements• Flamenco team

– Brycen Chun– Ame Elliott– Jennifer English– Kevin Li– Rashmi Sinha– Emilia Stoica– Kirsten Swearingen– Ping Yee

• Thanks also to NSF (IIS-9984741)

Thank you!

Marti HearstUC Berkeley School of Information

This Research Supported by NSF IIS-9984741.

Documents

Faceted Metadata in Search Interfacespeople.ischool.berkeley.edu/~hearst/talks/hearst_aaai05.pdf · Marti Hearst: UC Berkeley SIMS AAAI’05 Invited talk: Faceted Metadata in Search