Integration of Heterogeneous Databases without Common Domains Using Queries

Based on Textual Similarity:

William W. Cohen

Machine Learning Dept. and Language Technologies Inst.School of Computer ScienceCarnegie Mellon University

Embodied Cognition and Knowledge

What was that paper, and who is this guy talking?

Representation languages:

DBs, KR

Human languages:

NLP, IR

Machine Learning

WHIRLWord-Based HeterogeneousInformation Representation

Language

History

• 1982/1984: Ehud Shapiro’s thesis:

– MIS: Learning logic programs as debugging an empty Prolog program

– Thesis contained 17 figures and a 25-page appendix that were a full implementation of MIS in Prolog

– Incredibly elegant work

• “Computer science has a great advantage over other experimental sciences: the world we investigate is, to a large extent, our own creation, and we are the ones to determine if it is simple or messy.”

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History

• Grad school in AI at Rutgers• MTS at AT&T Bell Labs in group

doing KR, DB, learning, information retrieval, …

• My work: learning logical (description-logic-like, Prolog-like, rule-based) representations that model large noisy real-world datasets.

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History

• AT&T Bells Labs becomes AT&T Labs Research

• The web takes off – as predicted by Vinge and Gibson

• IR folks start looking at retrieval and question-answering with the Web

• Alon Halevy starts the Information Manifold project to integrate data on the web– VLDB 2006 10-year Best Paper

Award for 1996 paper on IM• I started thinking about the same

problem in a different way….

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History: WHIRL motivation 1

• As the world of computer science gets richer and more complex, computer science can no longer limit itself to studying “our own creation”.

• Tension exists between – Elegant theories of

representation

– The not-so-elegant real world that is being represented

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CA

History: WHIRL motivation 1

• The beauty of the real world is its complexity….82

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History: integration by mediation

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QuickTime™ and a decompressor

are needed to see this picture.

• Mediator translates between the knowledge in multiple separate KBs

• Each KB is a separate “symbol system”– No formal connection

between them except via the mediator

WHIRL idea: exploit linguistic properties of the HTML

“veneer” of web-accessible DBs 82

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QuickTime™ and a decompressor

are needed to see this picture.

TFIDF similarity

WHIRL Motivation 2: Web KBs are embodied

Link items asneeded 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.a S.a S.b T.b

Anhai Anhai Doan Doan

Dan Dan Weld Weld

Strongest links: those agreeable to most users

William Will Cohen Cohn

Steve Steven Minton Mitton

Weaker links: those agreeable to some users

William David Cohen Cohneven weaker links…

Link items asneeded 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.a S.a S.b T.b

Anhai Anhai Doan Doan

Dan Dan Weld 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.

William Will Cohen Cohn

Steve Steven Minton Mitton

William David Cohen Cohn

QuickTime™ and a decompressor

are needed to see this picture.

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

hitchhiker movie00137

the movie001,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”

After WHIRL

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• Efficient text joins

• On-the-fly, best-effort, imprecise integration

• Interactions between information extraction quality and results of queries on extracted data

• Keyword search on databases

• Use of statistics on text corpora to build intelligent “embodied” systems

• Turney: solving SAT analogies with PMI over word pairs

• Mitchell & Just: predicting FMI brain images resulting from reading a common noun (“hammer”) from co-occurrence information between nouns and verbs

Recent work: non-textual similarity

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“William W. Cohen, CMU”

“Dr. W. W. Cohen”

cohenwilliam w

drcmu

“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

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proposal

CMU

CALO

graph

William

6/18/07

6/17/07

Sent To

Term In Subject

einat@cs.cmu.edu

Learning to Search Email

[SIGIR 2006, CEAS 2006, WebKDD/SNA 2007]

Einat Minkov, CMU; Andrew Ng, Stanford

Tasks that are like similarity queries

Person namePerson namedisambiguationdisambiguation

ThreadingThreading

Alias findingAlias 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 email-addresses of Jason ?...

[ term Jason ]

“email-address”

Meeting Meeting attendees finderattendees finder

Which email-addresses (persons) should I notify about this meeting? [ meeting mtgId ]

“email-address”

Results on one task

0%

20%

40%

60%

80%

100%

1 2 3 4 5 6 7 8 9 10

Rank

Recall

Mgmt. game

PER

SO

N

NA

ME

DIS

AM

BIG

UA

TIO

N

Results on several tasks (MAP)

0.4

0.45

0.5

0.55

0.6

0.65

0.7

0.75

0.8

0.85

M.game sager Shapiro

0.4

0.45

0.5

0.55

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0.65

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0.75

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0.85

M.game Farmer Germany

0.4

0.45

0.5

0.55

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0.65

0.7

0.75

0.8

0.85

Meetings

Namedisambiguation

Threading

Alias finding

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*

*

*

*

*

*

*

** *

+

+

+

+ +

*

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. Canon2. Nikon3. Olympus

4. Pentax5. Sony6. Kodak7. Minolta8. Panasonic9. Casio10. Leica11. Fuji12. Samsung13. …

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

<li class=" acura"><a href="http://www. curryauto .com/" >

<li class=" nissan"><a href="http://www. curryauto.com/" >

<li class=" ford"><a href="http://www.curry auto.com/" > <img src="/common/logos/ ford/logo-horiz-rgb-lg-dkbg.gif" alt="3"></a> <ul><li class="last"><a href="http://www.curry auto.com/"> <span class="dName">Curry Ford</span>...</li></ul > </li>

<img src="/curryautogroup/images/logo -horiz-rgb-lg-dkbg.gif" alt="5"></a> <ul><li class="last"><a href="http://www.curryacura.com/" > <span class="dName">Curry Acura</span>...</li></ul> </li>

<img src="/common/logos/ nissan/logo-horiz-rgb-lg-dkbg.gif" alt="6"></a> <ul><li class="last"><a href= "http://www.geisau to.com/ "> <span class="dName">Curry Nissan </span>...</li></ul> </li>

Ranking Extractions

• 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 Email using Graphs. SIGIR 2006

Evaluation Method• Mean Average Precision

– Commonly used for evaluating ranked lists in IR– Contains recall and precision-oriented aspects– Sensitive to the entire ranking– Mean of average precisions for each ranked list

• Evaluation: Average over 36 datasets in three languages (Chinese, Japanese, English)

1. Average over several 2- or 3-seed queries for each dataset.

2. MAP performance: high 80s - mid 90s3. Google Sets: MAP in 40s, only English

where L = ranked list of extracted mentions, r = rank

Prec(r) = precision at rank r

(a) Extracted mention at r matches any true mention

(b) There exist no other extracted mention at rank less than r that is of the same entity as the one at r

⎩⎨⎧

=

otherwise

trueare (b) and (a) if

0

1

)(NewEntity r

# True Entities = total number of true entities in this dataset

Evaluation Datasets

Top three mentions are the seedsTry it out at http://rcwang.com/seal

Relational Set Expansion Seeds

Future?

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Representation languages:

DBs, KR

Human languages:

NLP, IR

Machine Learning

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