Focused CrawlingA New Approach to Topic-Specific

Web Resource Discovery

Soumen Chakrabarti

Martin van Den Berg

Byron Dom

WWW 1999 2

Portals and portholes

Popular search portals and directories Useful for generic needs Difficult to do serious research

Information needs of net-savvy users are getting very sophisticated

Relatively little business incentive Need handmade specialty sites: portholes Resource discovery must be personalized

WWW 1999 3

Quote

The emergence of portholes will be one of the major Internet trends of 1999. As people become more savvy users of the Net, they want things which are better focused on meeting their specific needs. We're going to see a whole lot more of this, and it's going to potentially erode the user base of some of the big portals.

Jim Hake(Founder, Global Information Infrastructure Awards)

WWW 1999 4

Scenario

Disk drive research group wants to track magnetic surface technologies

Compiler research group wants to trawl the web for graduate student resumés

____ wants to enhance his/her collection of bookmarks about ____ with prominent and relevant links

Virtual libraries like the Open Directory Project and the Mining Co.

WWW 1999 5

Structured web queries

How many links were found from an environment protection agency site to a site about oil and natural gas in the last year?

Apart from cycling, what is the most common topic cited by pages on cycling?

Find Web research pages which are widely cited by Hawaiian vacation pages

WWW 1999 6

Goal

Automatically construct a focused portal (porthole) containing resources that are Relevant to the user’s focus of interest Of high influence and quality Collectively comprehensive

Answer structured web queries by selectively exploring the topics involved in the query

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Tools at hand

Keyword search engines Synonymy, polysemy Abundance, lack of quality

Hand compiled topic directories Labor intensive, subjective judgements

Resources automatically located using keyword search and link graph distillation Dependence on large crawls and indices

WWW 1999 8

Estimating popularity

Extensive research on social network theory Wasserman and Faust

Hyperlink based Large in-degree indicates popularity/authority Not all votes are worth the same

Several similar ideas and refinements Googol (Page and Brin) and HITS (Kleinberg) Resource compilation (Chakrabarti et al) Topic distillation (Bharat and Henzinger)

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Topic distillation overview

Given web graph and query

Search engine selects sub-graph

Expansion, pruning and edge weights

Nodes iteratively transfer authority to cited neighbors

Search Engine Query

The Web

Selected subgraph

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Preliminary distillation-based approach

Design a keyword query to represent a topic Run topic distillation periodically Refine query through trial-and-error Works well if answer is partially known,

e.g., European airlines +swissair +iberia +klm

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Problems with preliminary approach

Dependence on large web crawl and index System = crawler + index + distiller

Unreliability of keyword match Engines differ significantly on a given query

due to small overlap [Bharat and Bröder] Narrow, arbitrary view of relevant subgraph Topic model does not improve over time

Difficulty of query construction Lack of output sensitivity

WWW 1999 13

Query construction

+“power suppl*”

“switch* mode” smps

-multiprocessor*

“uninterrupt* power suppl*” ups

-parcel*

/Companies/Electronics/Power_Supply

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Query complexity

Complex queries (966 trials) Average words 7.03 Average operators (+*–") 4.34

Typical Alta Vista queries are much simpler [Silverstein, Henzinger, Marais and Moricz] Average query words 2.35 Average operators (+*–") 0.41

Forcibly adding a hub or authority node helped in 86% of the queries

WWW 1999 15

Query complexity Complex queries

needed for distillation Typical Alta Vista

queries are much simpler (Silverstein, Henzinger, Marais and Moricz)

Forcing a hub or authority helps 86% of the time

Dis

tilla

tion

Alta

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ord

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7.03

4.34

2.35

0.410

2

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Operators Words

WWW 1999 16

Output sensitivity

Say the goal is to find a comprehensive collection of recreational and competitive bicycling sites and pages

Ideally effort should scale with size of the result

Time spent crawling and indexing sites unrelated to the topic is wasted

Likewise, time that does not improve comprehensiveness is wasted

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Proposed solution

Resource discovery system that can be customized to crawl for any topic by giving examples

Hypertext mining algorithms learn to recognize pages and sites about the given topic, and a measure of their centrality

Crawler has guidance hooks controlled by these two scores

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Administration scenario

TaxonomyEditor

CurrentExamples

SuggestedAdditionalExamples

Drag

WWW 1999 19

Relevance

All

Bus&Econ Recreation

Companies Cycling

Bike Shops

Mt.Biking

Clubs

Arts

... ...

Path nodes

Good nodesSubsumed nodes

)good(

]|Pr[]good is Pr[c

dcd

WWW 1999 20

Classification

How relevant is a document w.r.t. a class? Supervised learning, filtering, classification,

categorization

Many types of classifiers Bayesian, nearest neighbor, rule-based

Hypertext Both text and links are class-dependent clues How to model link-based features?

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The “bag-of-words” document model

Decide topic; topic c is picked with prior probability (c); c(c) = 1

Each c has parameters (c,t) for terms t Coin with face probabilities t (c,t) = 1

Fix document length and keep tossing coin Given c, probability of document is

dt

tdntctdn

dncd ),(),(

)},({

)(]|Pr[

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Exploiting link features

c=class, t=text, N=neighbors

Text-only model: Pr[t|c] Using neighbors’ text

to judge my topic:Pr[t, t(N) | c]

Better model:Pr[t, c(N) | c]

Non-linear relaxation

?

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Improvement using link features

9600 patents from 12 classes marked by USPTO

Patents have text and cite other patents

Expand test patent to include neighborhood

‘Forget’ fraction of neighbors’ classes

0

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0 50 100

%Neighborhood known%

Err

or

Text Link Text+Link

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Putting it together

TaxonomyDatabase

TaxonomyEditor

ExampleBrowser

CrawlDatabase

HypertextClassifier(Learn)

TopicModels

HypertextClassifier(Apply)

Scheduler

Workers

TopicDistiller

Feedback

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Monitoring the crawler

Time

Relevance

One URL

MovingAverage

WWW 1999 26

Measures of success

Harvest rate What fraction of crawled pages are relevant

Robustness across seed sets Separate crawls with random disjoint samples Measure overlap in URLs and servers crawled Measure agreement in best-rated resources

Evidence of non-trivial work #Links from start set to the best resources

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Harvest rateHarvest Rate (Cycling, Unfocused)

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Avg over 100

Harvest Rate (Cycling, Soft Focus)

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Unfocused Focused

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Crawl robustness

Crawl Robustness (Cycling)

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Crawl Robustness (Cycling)

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Overlap1

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URL Overlap Server OverlapCrawl 1 Crawl 2

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Top resources after one hour

Recreational and competitive cycling http://www.truesport.com/Bike/links.htm http://reality.sgi.com/billh_hampton/jrvs/links.html http://www.acs.ucalgary.ca/~bentley/mark_links.html

HIV/AIDS research and treatment http://www.stopaids.org/Otherorgs.html http://www.iohk.com/UserPages/mlau/aidsinfo.html http://www.ahandyguide.com/cat1/a/a66.htm

Purer and better than root set

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Distance to best resources

Resource Distance (Mutual Funds)

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Min. distance from crawl seed (#links)

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Resource Distance (Cycling)

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Min. distance from crawl seed (#links)

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Cycling: cooperative Mutual funds: competitive

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Robustness of resource discovery

Sample disjoint sets of starting URL’s

Two separate crawls Find best authorities Order by rank Find overlap in the

top-rated resources

Resource Robustness (Cycling)

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Overlap1

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Related work

WebWatcher, HotList and ColdList Filtering as post-processing, not acquisition

ReferralWeb Social network on the Web

Ahoy!, Cora Hand-crafted to find home pages and papers

WebCrawler, Fish, Shark, Fetuccino, agents Crawler guided by query keyword matches

WWW 1999 35

Comparison with agents Agents usually look

for keywords and hand-crafted patterns

Cannot learn new vocabulary dynamically

Do not use distance-2 centrality information

Client-side assistant

We use taxonomy with statistical topic models

Models can evolve as crawl proceeds

Combine relevance and centrality

Broader scope: inter-community linkage analysis and querying

WWW 1999 36

Conclusion

New architecture for example-driven topic-specific web resource discovery

No dependence on full web crawl and index Modest desktop hardware adequate Variable radius goal-directed crawling High harvest rate High quality resources found far from

keyword query response nodes