Using Machine Learning to Discover and Understand Structured Data William W. Cohen Machine Learning Dept. and Language Technologies Inst. School of Computer Science Carnegie Mellon University

Outline Information integration: –Some history –The problem, the economics, and the economic problem “Soft” information integration Concrete uses of “soft” integration –Classification –Collaborative filtering –Set expansion

WHIRL project ( ) WHIRL initiated when at AT&T Bell Labs AT&T Research AT&T Labs - Research AT&T Labs AT&T Research AT&T Research – Shannon Laboratory AT&T Shannon Labs

When are two entities the same? Bell Labs Bell Telephone Labs AT&T Bell Labs A&T Labs AT&T Labs—Research AT&T Labs Research, Shannon Laboratory Shannon Labs Bell Labs Innovations Lucent Technologies/Bell Labs Innovations History of Innovation: From 1925 to today, AT&T has attracted some of the world's greatest scientists, engineers and developers…. [ Bell Labs Facts: Bell Laboratories, the research and development arm of Lucent Technologies, has been operating continuously since 1925… [bell-labs.com] [1925]

In the once upon a time days of the First Age of Magic, the prudent sorcerer regarded his own true name as his most valued possession but also the greatest threat to his continued good health, for--the stories go-- once an enemy, even a weak unskilled enemy, learned the sorcerer's true name, then routine and widely known spells could destroy or enslave even the most powerful. As times passed, and we graduated to the Age of Reason and thence to the first and second industrial revolutions, such notions were discredited. Now it seems that the Wheel has turned full circle (even if there never really was a First Age) and we are back to worrying about true names again: The first hint Mr. Slippery had that his own True Name might be known-- and, for that matter, known to the Great Enemy--came with the appearance of two black Lincolns humming up the long dirt driveway... Roger Pollack was in his garden weeding, had been there nearly the whole morning.... Four heavy-set men and a hard-looking female piled out, started purposefully across his well-tended cabbage patch.… This had been, of course, Roger Pollack's great fear. They had discovered Mr. Slippery's True Name and it was Roger Andrew Pollack TIN/SSAN

“Buddhism rejects the key element in folk psychology: the idea of a self (a unified personal identity that is continuous through time)… King Milinda and Nagasena (the Buddhist sage) discuss … personal identity… Milinda gradually realizes that "Nagasena" (the word) does not stand for anything he can point to: … not … the hairs on Nagasena's head, nor the hairs of the body, nor the "nails, teeth, skin, muscles, sinews, bones, marrow, kidneys,..." etc… Milinda concludes that "Nagasena" doesn't stand for anything… If we can't say what a person is, then how do we know a person is the same person through time? … There's really no you, and if there's no you, there are no beliefs or desires for you to have… The folk psychology picture is profoundly misleading and believing it will make you miserable.” -S. LaFave When are two entities are the same?

Deduction via co-operation Site1 Site2 Site3 KB1 KB2 KB3 Standard Terminology Integrated KB User Economic issues: Who pays for integration? Who tracks errors & inconsistencies? Who fixes bugs? Who pushes for clarity in underlying concepts and object identifiers? Standards approach  publishers are responsible  publishers pay Mediator approach: 3 rd party does the work, agnostic as to cost

Linkage Queries Traditional approach: Uncertainty about what to link must be decided by the integration system, not the end user

Link items as needed by Q Query Q SELECT R.a,S.a,S.b,T.b FROM R,S,T WHERE R.a=S.a and S.b=T.b R.aS.aS.bT.b Anhai Doan Dan Weld Strongest links: those agreeable to most users WilliamWillCohenCohn SteveStevenMintonMitton Weaker links: those agreeable to some users WilliamDavidCohenCohn even weaker links… WHIRL approach:

Link items as needed by Q WHIRL approach: Query Q SELECT R.a,S.a,S.b,T.b FROM R,S,T WHERE R.a~S.a and S.b~T.b (~ TFIDF-similar) R.aS.aS.bT.b Anhai Doan Dan Weld Incrementally produce a ranked list of possible links, with “best matches” first. User (or downstream process) decides how much of the list to generate and examine. WilliamWillCohenCohn SteveStevenMintonMitton WilliamDavidCohenCohn

WHIRL queries Assume two relations: review(movieTitle,reviewText): archive of reviews listing(theatre, movieTitle, showTimes, …): now showing The Hitchhiker’s Guide to the Galaxy, 2005 This is a faithful re-creation of the original radio series – not surprisingly, as Adams wrote the screenplay …. Men in Black, 1997 Will Smith does an excellent job in this … Space Balls, 1987 Only a die-hard Mel Brooks fan could claim to enjoy … …… Star Wars Episode III The Senator Theater 1:00, 4:15, & 7:30pm. Cinderella Man The Rotunda Cinema 1:00, 4:30, & 7:30pm. ………

WHIRL queries “Find reviews of sci-fi comedies [movie domain] FROM review SELECT * WHERE r.text~’sci fi comedy’ (like standard ranked retrieval of “sci-fi comedy”) “ “Where is [that sci-fi comedy] playing?” FROM review as r, LISTING as s, SELECT * WHERE r.title~s.title and r.text~’sci fi comedy’ (best answers: titles are similar to each other – e.g., “Hitchhiker’s Guide to the Galaxy” and “The Hitchhiker’s Guide to the Galaxy, 2005” and the review text is similar to “sci-fi comedy”)

WHIRL queries Similarity is based on TFIDF  rare words are most important. Search for high-ranking answers uses inverted indices…. The Hitchhiker ’s Guide to the Galaxy, 2005 Men in Black, 1997 Space Balls, 1987 … Star Wars Episode III Hitchhiker ’s Guide to the Galaxy Cinderella Man … Years are common in the review archive, so have low weight hitchhikermovie00137 themovie001,movie003,movie007,movie008, movie013,movie018,movie023,movie0031, ….. - It is easy to find the (few) items that match on “ important ” terms - Search for strong matches can prune “unimportant terms”

Outline Information integration: –Some history –The problem, the economics, and the economic problem “Soft” information integration Concrete uses of “soft” integration –Classification –Collaborative filtering –Set expansion

Outline Information integration: –Some history –The problem, the economics, and the economic problem “Soft” information integration Concrete uses of “soft” integration –Classification –Collaborative filtering –Set expansion

Outline Information integration: –Some history –The problem, the economics, and the economic problem “Soft” information integration Concrete uses of “soft” integration –Classification –Collaborative filtering –Set expansion: using generalized notion of similarity

Recent work: non-textual similarity “William W. Cohen, CMU” “Dr. W. W. Cohen” cohen williamw dr cmu “George W. Bush” “George H. W. Bush” “Christos Faloutsos, CMU”

Recent work Personalized PageRank aka Random Walk with Restart: –Similarity measure for nodes in a graph, analogous to TFIDF for text in a WHIRL database –natural extension to PageRank –amenable to learning parameters of the walk (gradient search, w/ various optimization metrics): Toutanova, Manning & NG, ICML2004; Nie et al, WWW2005; Xi et al, SIGIR 2005 –various speedup techniques exist –queries: Given type t* and node x, find y:T(y)=t* and y~x

proposal CMU CALO graph William 6/18/07 6/17/07 Sent To Term In Subject Learning to Search [SIGIR 2006, CEAS 2006, WebKDD/SNA 2007] Einat Minkov, CMU; Andrew Ng, Stanford

Tasks that are like similarity queries Person name disambiguation Threading Alias finding [ term “andy” file msgId ] “person” [ file msgId ] “file”  What are the adjacent messages in this thread?  A proxy for finding “more messages like this one” What are the -addresses of Jason ?... [ term Jason ] “ -address” Meeting attendees finder Which -addresses (persons) should I notify about this meeting? [ meeting mtgId ] “ -address”

Learning to search better Query a  node rank 1  node rank 2  node rank 3  node rank 4  …  node rank 10  node rank 11  node rank 12  …  node rank 50 Query b Query q  node rank 1  node rank 2  node rank 3  node rank 4  …  node rank 10  node rank 11  node rank 12  …  node rank 50  node rank 1  node rank 2  node rank 3  node rank 4  …  node rank 10  node rank 11  node rank 12  …  node rank 50 … GRAPH WALK + Rel. answers a+ Rel. answers b+ Rel. answers q Task T (query class)

Graph walk Feature generation Learn re-ranker Re-ranking function Learning Node re-ordering: train task

Learning Approach train task Graph walk Feature generation Learn re-ranker Re-ranking function Graph walk Feature generation Score by re-ranking function Node re-ordering: Boosting test task [Collins & Koo, CL 2005; Collins, ACL 2002] Voted Perceptron; RankSVM; PerceptronCommittees; … [Joacchim KDD 2002, Elsas et al WSDM 2008]

Graph walk Weight update Theta* Learning approaches Edge weight tuning:

Graph walk Weight update Graph walk Learning approaches Edge weight tuning: Theta* task Graph walk Feature generation Learn re-ranker Re-ranking function Graph walk Feature generation Score by re-ranking function Node re-ordering: Boosting; Voted Perceptron Question: which is better? [Diligenti et al, IJCAI 2005; Toutanova & Ng, ICML 2005; … ]

Results on one task Mgmt. game PERSON NAME DISAMBIGUATION

Results on several tasks (MAP) Name disambiguation Threading Alias finding * * * * * * * * * * * *

Set Expansion using the Web Fetcher: download web pages from the Web Extractor: learn wrappers from web pages Ranker: rank entities extracted by wrappers 1.Canon 2.Nikon 3.Olympus 4.Pentax 5.Sony 6.Kodak 7.Minolta 8.Panasonic 9.Casio 10.Leica 11.Fuji 12.Samsung 13.… Richard Wang, CMU

The Extractor Learn wrappers from web documents and seeds on the fly –Utilize semi-structured documents –Wrappers defined at character level No tokenization required; thus language independent However, very specific; thus page-dependent –Wrappers derived from document d is applied to d only

Building a Graph A graph consists of a fixed set of… –Node Types: {seeds, document, wrapper, mention} –Labeled Directed Edges: {find, derive, extract} Each edge asserts that a binary relation r holds Each edge has an inverse relation r -1 (graph is cyclic) “ford”, “nissan”, “toyota” curryauto.com Wrapper #3 Wrapper #2 Wrapper #1 Wrapper #4 “honda” 26.1% “acura” 34.6% “chevrolet” 22.5% “bmw pittsburgh” 8.4% “volvo chicago” 8.4% find derive extract northpointcars.com Minkov et al. Contextual Search and Name Disambiguation in using Graphs. SIGIR 2006

Top three mentions are the seeds Try it out at

Relational Set Expansion Seeds

Additional relevant research Alon Halevey and friends: –“Pay as you go”, “on the fly”, data integration (e.g., SIGMOD 98): integrate partially, then allow user to perform search to make up for inaccuracy of result Anhai Doan and friends: –“Best effort” information extraction (SIGMOD 98): write an approximate program for extraction from web pages, then allow user to perform search to make up for inaccuracy of result Semi-structured extensions: –Kushmeric’s ELIXIR (SIGIR 2001); Bernstein’s iSPARQL (eg ESWC 2008) Soft joins: –Gravano et al WWW2003: Text Joints in an RDMS for Web Data Integration –Bayardo et al, WWW2007: Scaling up all-pairs similarity search. –Koudas et al, SIGMOD 2006: Record linkage: similarity measures and algorithms (survey)

Outline Information integration: –Some history –The problem, the economics, and the economic problem “Soft” information integration Concrete uses of “soft” integration –Classification –Collaborative filtering –Set expansion –Questions?