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Hypertext Databases and Data Mining (SIGMOD 1999 Tutorial)

Hypertext Databases and Data Mining (SIGMOD 1999 Tutorial). Soumen Chakrabarti Indian Institute of Technology Bombay http://www.cse.iitb.ernet.in/~soumen http://www.cs.berkeley.edu/~soumen soumen@cse.iitb.ernet.in. The Web. 350 million static HTML pages, 2 terabytes

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Hypertext Databases and Data Mining (SIGMOD 1999 Tutorial)

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  1. Hypertext Databases and Data Mining(SIGMOD 1999 Tutorial) Soumen Chakrabarti Indian Institute of Technology Bombay http://www.cse.iitb.ernet.in/~soumenhttp://www.cs.berkeley.edu/~soumensoumen@cse.iitb.ernet.in

  2. The Web • 350 million static HTML pages, 2 terabytes • 0.8–1 million new pages created per day • 600 GB of pages change per month • Average page changes in a few weeks • Average page has about ten links • Increasing volume of active pages and views • Boundaries between repositories blurred • Bigger than the sum of its parts

  3. Hypertext databases • Academia • Digital library, web publication • Consumer • Newsgroups, communities, product reviews • Industry and organizations • Health care, customer service • Corporate email

  4. What to expect • Write in decimal the exact circumference of a circle of radius one inch • Is the distance between Tokyo and Rome more than 6000 miles? • What is the distance between Tokyo and Rome? • java • java +coffee -applet • “uninterrupt* power suppl*” ups -parcel

  5. Verity Fulcrum PLS Oracle text extender DB2 text extender Infoseek Intranet SMART (academic) Glimpse (academic) Inktomi (HotBot) Alta Vista Google! Yahoo! Infoseek Internet Lycos Excite Search products and services

  6. Local data FTP Gopher HTML More structure IndexingSearch Crawling WebSQL WebL Relevance Ranking Social Network of Hyperlinks Latent Semantic Indexing XML Clustering Web Communities Collaborative Filtering Scatter- Gather Web Servers Topic Distillation Topic Directories Monitor Mine Modify User Profiling Semi-supervised Learning Automatic Classification Focused Crawling Web Browsers

  7. Basic indexing and search

  8. Keyword indexing • Boolean search • care AND NOT old • Stemming • gain* • Phrases and proximity • “new care” • loss <NEAR/5> care • <SENTENCE> My care is loss of care with old care done D1 Your care is gain of care with new care won D2 care D1: 1, 5, 8 D2: 1, 5, 8 new D2: 7 D1: 7 old loss D1: 3

  9. Tables and queries 1 POSTING select distinct did from POSTING where tid = ‘care’ except select distinct did from POSTING where tid like ‘gain%’ with TPOS1(did, pos) as (select did, pos from POSTING where tid = ‘new’), TPOS2(did, pos) as (select did, pos from POSTING where tid = ‘care’) select distinct did from TPOS1, TPOS2 where TPOS1.did = TPOS2.did and proximity(TPOS1.pos, TPOS2.pos) proximity(a, b) ::= a + 1 = b abs(a - b) < 5

  10. Relevance ranking • Recall = coverage • What fraction of relevant documents were reported • Precision = accuracy • What fraction of reported documents were relevant • Trade-off Query “True response” Compare Search Consider prefix k Output sequence

  11. Vector space model and TFIDF • Some words are more important than others • W.r.t. a document collection D • d+ have a term, d- do not • “Inverse document frequency” • “Term frequency” (TF) • Many variants: • Probabilistic models

  12. Tables and queries 2 VECTOR(did, tid, elem) ::= With TEXT(did, tid, freq) as (select did, tid, count(distinct pos) from POSTING group by did, tid), LENGTH(did, len) as (select did, sum(freq) from TEXT group by did), DOCFREQ(tid, df) as (select tid, count(distinct did) from TEXT group by tid) select did, tid, (freq / len) * (1 + log((select count(distinct did from POSTING))/df)) from TEXT, LENGTH, DOCFREQ where TEXT.did = LENGTH.did and TEXT.tid = DOCFREQ.tid

  13. Relevance ranking ‘now’ select did, cosine(did, query) from corpus where candidate(did, query) order by cosine(did, query) desc fetch first k rows only query ‘auto’ ‘car’ Find largest k columns of: D Exact computation: O(n2) All entries above mean can be estimated with error e within O(ne-2) time A T

  14. Similarity and clustering

  15. Clustering • Given an unlabeled collection of documents, induce a taxonomy based on similarity • Need document similarity measure • Distance between normalized document vectors • Cosine of angle between document vectors • Top-down clustering is difficult because of huge number of noisy dimensions • k-means, expectation maximization • Quadratic-time bottom-up clustering

  16. Document model • Vocabulary V, term wi, document  represented by • is the number of times wi occurs in document  • Most f’s are zeroes for a single document • Monotone component-wise damping function g such as log or square-root

  17. Similarity Normalized document profile: Profile for document group :

  18. Group average clustering 1 • Initially G is a collection of singleton groups, each with one document • Repeat • Find ,  in G with max s() • Merge group  with group  • For each  keep track of best  • O(n2) algorithm

  19. Group average clustering 2 Un-normalizedgroup profile: Can show:

  20. “Rectangular time” algorithm Buckshot • Randomly sample documents • Run group average clustering algorithm to reduce to k groups or clusters • Iterate assign-to-nearest O(1) times • Move each document to cluster  with max s(,) • Total time taken is O(kn)

  21. Extended similarity • auto and car co-occur often • Therefore they must be related • Documents having related words are related • Useful for search and clustering • Two basic approaches • Hand-made thesaurus (WordNet) • Co-occurrence and associations … auto …car … car … auto … auto …car … car … auto … auto …car … car … auto car  auto … auto …  … car …

  22. k k-dim vector Latent semantic indexing Term Document d Documents A U D V SVD Terms t d r

  23. Collaborative recommendation • People=record, movies=features, cluster people • Both people and features can be clustered • For hypertext access, time of access is a feature • Need advanced models

  24. A model for collaboration • People and movies belong to unknown classes • Pk = probability a random person is in class k • Pl = probability a random movie is in class l • Pkl = probability of a class-k person liking a class-l movie • Gibbs sampling: iterate • Pick a person or movie at random and assign to a class with probability proportional to Pk or Pl • Estimate new parameters

  25. Supervised learning

  26. Supervised learning (classification) • Many forms • Content: automatically organize the web per Yahoo! • Type: faculty, student, staff • Intent: education, discussion, comparison, advertisement • Applications • Relevance feedback for re-scoring query responses • Filtering news, email, etc. • Narrowing searches and selective data acquisition

  27. Difficulties • Dimensionality • Decision tree classifiers: dozens of columns • Vector space model: 50,000 ‘columns’ • Context-dependent noise • ‘Can’ (v.) considered a ‘stopword’ • ‘Can’ (n.) may not be a stopword in/Yahoo/SocietyCulture/Environment/Recycling

  28. More difficulties • Need for scalability • High dimension needs more data to learn • Class labels are from a hierarchy • All documents belong to the root node • Highest probability leaf may have low confidence

  29. Techniques • Nearest neighbor • Standard keyword index also supports classification • How to define similarity? (TFIDF may not work) • Wastes space by storing individual document info • Rule-based, decision-tree based • Very slow to train (but quick to test) • Good accuracy (but brittle rules) • Model-based • Fast training and testing with small footprint

  30. More document models • Boolean vector (word counts ignored) • Toss one coin for each term in the universe • Bag of words (multinomial) • Repeatedly toss coin with a term on each face • Limited dependence models • Bayesian network where each feature has at most k features as parents • Maximum entropy estimation

  31. “Bag-of-words” • Decide topic; topic c is picked with prior probability (c); c(c) = 1 • Each topic 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

  32. Limitations • With the model • 100th occurrence of term as surprising as first • No inter-term dependence • With using the model • Most observed (c,t) are zero and/or noisy • Have to pick a low-noise subset of the term universe • Improves space, time, and accuracy • Have to “fix” low-support statistics

  33. Feature selection Model with unknown parameters Confidence intervals T T p1 p1 p2 ... q1 q2 ... q1 N Observed data 0 1 ... Pick FT such that models built over F have high separation confidence N

  34. Tables and queries 3 TAXONOMY EGMAPR(did, kcid) ::= ((select did, kcid from EGMAP) union all (select e.did, t.pcid from EGMAPR as e, TAXONOMY as t where e.kcid = t.kcid)) STAT(pcid, tid, kcid, ksmc, ksnc) ::= (select pcid, tid, TAXONOMY.kcid, count(distinct TEXT.did), sum(freq) from EGMAPR, TAXONOMY, TEXT where TAXONOMY.kcid = EGMAPR.kcid and EGMAPR.did = TEXT.did group by pcid, tid, TAXONOMY.kcid) 1 2 3 EGMAP 4 5 TEXT

  35. Analyzing hyperlink structure

  36. Hyperlink graph analysis • Hypermedia is a social network • Telephoned, advised, co-authored, paid, cited • Social network theory (cf. Wasserman & Faust) • Extensive research applying graph notions • Centrality • Prestige and reflected prestige • Co-citation • Can be applied directly to Web search • HIT, Google, CLEVER, topic distillation

  37. Hypertext models for classification • 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 ?

  38. Exploiting 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

  39. Google and HITS • In-degree  prestige • Not all votes are worth the same • Prestige of a page is the sum of prestige of citing pages: p = Ep • Pre-compute query independent prestige score • High prestige  good authority • High reflected prestige  good hub • Bipartite iteration • a = Eh • h = ETa • h = ETEh

  40. Tables and queries 4 delete from HUBS; insert into HUBS(url, score) (select urlsrc, sum(score * wtrev) from AUTH, LINK where authwt is not null and type = non-local and ipdst <> ipsrc and url = urldst group by urlsrc); update HUBS set (score) = score / (select sum(score) from HUBS); HUBS AUTH update LINK as X set (wtfwd) = 1. / (select count(ipsrc) from LINK where ipsrc = X.ipsrc and urldst = X.urldst) where type = non-local; wgtfwd score score urlsrc @ipsrc urldst @ipdst LINK wgtrev

  41. Querying/mining semi-structured data

  42. Semi-structured database systems • Lore (Stanford) • Object exchange model, dataguides • WebSQL (Toronto), WebL (Compaq SRC) • Structured query languages for the Web • WHIRL (AT&T Research) • Approximate matches on multiple textual columns • Strudel (AT&T Research, U. Washington) • Web site generation and management

  43. Queries combining structure and content • Select x.url, x.title from Document x such that “http://www.cs.wisc.edu”==||  x where x mentions “semi-structured data” • Apart from cycling, find the most common topic found within link radius 2 of pages on cycling • In the last year, how many links were made from environment protection pages to Exxon? Answer: “first-aid”

  44. Taxonomy Editor Example Browser Topic Distiller Scheduler Feedback Taxonomy Database Crawl Database Crawler Workers Hypertext Classifier (Learn) Hypertext Classifier (Apply) TopicModels Resource discovery

  45. Resource discovery results 1 • High rate of ‘harvesting’ relevant pages • Standard crawling neither necessary nor adequate for answering specific queries

  46. Resource discovery results 2 • Robust to perturbations of starting URLs • Great resources found 12 links from start set

  47. Database issues • Useful features • Concurrency and recovery (crawlers) • I/O-efficient representation of mining algorithms • Ad-hoc queries combining structure and content • Need better support for • Flexible choices for concurrency and recovery • Indexed scans over temporary table expressions • Efficient string storage and operations • Answering complex queries approximately

  48. Resources

  49. Research areas • Modeling, representation, and manipulation • More applications of machine learning • Approximate structure and content matching • Answering questions in specific domains • Interactive refinement of ill-defined queries • Tracking emergent topics in a discussion group • Content-based collaborative recommendation • Semantic prefetching and caching

  50. Events and activities • Text REtrieval Conference (TREC) • Mature ad-hoc query and filtering tracks (newswire) • New track for web search (2GB and 100GB corpus) • New track for question answering • DIMACS special years on Networks (-2000) • Includes applications such as information retrieval, databases and the Web, multimedia transmission and coding, distributed and collaborative computing • Conferences: WWW, SIGIR, SIGMOD/VLDB?

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