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Focused Crawling A New Approach to Topic-Specific Web Resource Discovery. Soumen Chakrabarti Martin van Den Berg Byron Dom. Portals and portholes. Popular search portals and directories Useful for generic needs Difficult to do serious research
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Focused CrawlingA New Approach to Topic-SpecificWeb Resource Discovery Soumen Chakrabarti Martin van Den Berg Byron Dom
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
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)
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.
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
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
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
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)
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 The Web Search Engine Query Selected subgraph
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
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
Query construction /Companies/Electronics/Power_Supply +“power suppl*” “switch* mode” smps -multiprocessor* “uninterrupt* power suppl*” ups -parcel*
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
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
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
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
Administration scenario Current Examples Drag Taxonomy Editor Suggested Additional Examples
Relevance Path nodes All Arts Bus&Econ Recreation Companies ... Cycling ... Bike Shops Clubs Mt.Biking Good nodes Subsumed nodes
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?
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
Exploiting link features • c=class, t=text, N=neighbors • Text-only model: Pr[t|c] • Using neighbors’ textto judge my topic:Pr[t, t(N) | c] • Better model:Pr[t, c(N)| c] • Non-linear relaxation ?
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
Taxonomy Editor Example Browser Topic Distiller Scheduler Feedback Taxonomy Database Crawl Database Workers Hypertext Classifier (Learn) Hypertext Classifier (Apply) TopicModels Putting it together
Monitoring the crawler One URL Relevance Moving Average Time
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
Harvest rate Unfocused Focused
Crawl robustness URL Overlap Server Overlap Crawl 1 Crawl 2
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
Cycling: cooperative Mutual funds: competitive Distance to best resources
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
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
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 Comparison with agents
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