Alon Halevy

University of Washington

Joint work with Anhai Doan and Pedro Domingos

Learning to Map Between Learning to Map Between SchemasSchemas

Ontologies Ontologies

2

AgendaAgenda Ontology mapping is a key problem in many

applications:– Data integration– Semantic web– Knowledge management– E-commerce

LSD:– Solution that uses multi-strategy learning.– We’ve started with schema matching (I.e., very simple

ontologies)– Currently extending to more expressive ontologies.– Experiments show the approach is very promising!

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The The Structure Structure Mapping ProblemMapping Problem

Types of structures:– Database schemas, XML DTDs, ontologies, …,

Input:– Two (or more) structures, S1 and S2

– Data instances for S1 and S2

– Background knowledge

Output:– A mapping between S1 and S2

– Should enable translating between data instances.

– Semantics of mapping?

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Semantic Mappings between SchemasSemantic Mappings between Schemas Source schemas = XML DTDs

house

location contact

house

address

name phone

num-baths

full-baths half-baths

contact-info

agent-name agent-phone

1-1 mapping non 1-1 mapping

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MotivationMotivation Database schema integration

– A problem as old as databases themselves. – database merging, data warehouses, data migration

Data integration / information gathering agents– On the WWW, in enterprises, large science projects

Model management:– Model matching: key operator in an algebra where

models and mappings are first-class objects.– See [Bernstein et al., 2000] for more.

The Semantic Web– Ontology mapping.

System interoperability– E-services, application integration, B2B applications, …,

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Desiderata from Proposed SolutionsDesiderata from Proposed Solutions Accuracy, efficiency, ease of use. Realistic expectations:

– Unlikely to be fully automated. Need user in the loop.

Some notion of semantics for mappings. Extensibility:

– Solution should exploit additional background knowledge.

“Memory”, knowledge reuse:– System should exploit previous manual or automatically

generated matchings.– Key idea behind LSD.

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LSD OverviewLSD Overview L(earning) S(ource) D(escriptions) Problem: generating semantic mappings between

mediated schema and a large set of data source schemas.

Key idea: generate the first mappings manually, and learn from them to generate the rest.

Technique: multi-strategy learning (extensible!) Step 1:

– [SIGMOD, 2001]: 1-1 mappings between XML DTDs. Current focus:

– Complex mappings– Ontology mapping.

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OutlineOutline Overview of structure mapping

Data integration and source mappings

LSD architecture and details

Experimental results

Current work.

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Data IntegrationData Integration

Find houses with four bathrooms priced under $500,000

mediated schema

homes.comrealestate.com

source schema 2

homeseekers.com

source schema 3source schema 1

Applications: WWW, enterprises, science projectsTechniques: virtual data integration, warehousing, custom code.

wrappers

Query reformulationand optimization.

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Semantic Mappings between SchemasSemantic Mappings between Schemas Source schemas = XML DTDs

house

location contact

house

address

name phone

num-baths

full-baths half-baths

contact-info

agent-name agent-phone

1-1 mapping non 1-1 mapping

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Semantics (preliminary)Semantics (preliminary) Semantics of mappings has received no attention. Semantics of 1-1 mappings – Given:

– R(A1,…,An) and S(B1,…,Bm)– 1-1 mappings (Ai,Bj)

Then, we postulate the existence of a relation W, s.t.:– (C1,…,Ck) (W) = (A1,…,Ak) (R) , – (C1,…,Ck) (W) = (B1,…,Bk) (S) , – W also includes the unmatched attributes of R and S.

In English: R and S are projections on some universal relation W, and the mappings specify the projection variables and correspondences.

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Why Matching is DifficultWhy Matching is Difficult Aims to identify same real-world entity

– using names, structures, types, data values, etc Schemas represent same entity differently

– different names => same entity: – area & address => location

– same names => different entities: – area => location or square-feet

Schema & data never fully capture semantics!– not adequately documented, not sufficiently expressive

Intended semantics is typically subjective!– IBM Almaden Lab = IBM?

Cannot be fully automated. Often hard for humans. Committees are required!

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Current State of AffairsCurrent State of Affairs

Finding semantic mappings is now the bottleneck!– largely done by hand– labor intensive & error prone– GTE: 4 hours/element for 27,000 elements [Li&Clifton00]

Will only be exacerbated– data sharing & XML become pervasive– proliferation of DTDs– translation of legacy data– reconciling ontologies on semantic web

Need semi-automatic approaches to scale up!

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OutlineOutline Overview of structure mapping

Data integration and source mappings

LSD architecture and details

Experimental results

Current work.

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The LSD ApproachThe LSD Approach User manually maps a few data sources to the

mediated schema. LSD learns from the mappings, and proposes

mappings for the rest of the sources. Several types of knowledge are used in learning:

– Schema elements, e.g., attribute names– Data elements: ranges, formats, word frequencies, value

frequencies, length of texts.– Proximity of attributes– Functional dependencies, number of attribute

occurrences. One learner does not fit all. Use multiple learners

and combine with meta-learner.

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listed-price $250,000 $110,000 ...

address price agent-phone description

Example Example

location Miami, FL Boston, MA ...

phone(305) 729 0831(617) 253 1429 ...

commentsFantastic houseGreat location ...

realestate.com

location listed-price phone comments

Schema of realestate.com

If “fantastic” & “great”

occur frequently in data values =>

description

Learned hypotheses

price $550,000 $320,000 ...

contact-phone(278) 345 7215(617) 335 2315 ...

extra-infoBeautiful yardGreat beach ...

homes.com

If “phone” occurs in the name =>

agent-phone

Mediated schema

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Multi-Strategy LearningMulti-Strategy Learning

Use a set of base learners:– Name learner, Naïve Bayes, Whirl, XML learner

And a set of recognizers:– County name, zip code, phone numbers.

Each base learner produces a prediction weighted by confidence score.

Combine base learners with a meta-learner, using stacking.

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Name Learner

Base LearnersBase Learners

(contact,agent-phone)

(contact-info,office-address)

(phone,agent-phone)(listed-price,price)

contact-phone => (agent-phone,0.7), (office-address,0.3)

Naive Bayes Learner [Domingos&Pazzani 97]

– “Kent, WA” => (address,0.8), (name,0.2)

Whirl Learner [Cohen&Hirsh 98]

XML Learner– exploits hierarchical structure of XML data

(contact,agent-phone)

(contact-info,office-address)

(phone,agent-phone)(listed-price,price)

(contact-phone, ? )

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<location> Boston, MA </> <listed-price> $110,000</> <phone> (617) 253 1429</> <comments> Great location </>

<location> Miami, FL </> <listed-price> $250,000</> <phone> (305) 729 0831</> <comments> Fantastic house </>

Training the Base Learners Training the Base Learners

Naive Bayes Learner

(location, address)(listed-price, price)(phone, agent-phone) ...

(“Miami, FL”, address)(“$ 250,000”, price)(“(305) 729 0831”, agent-phone) ...

realestate.com

Name Learner

address price agent-phone description

Schema of realestate.com

Mediated schema

location listed-price phone comments

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Entity RecognizersEntity RecognizersUse pre-programmed knowledge to identify

specific types of entities– date, time, city, zip code, name, etc– house-area (30 X 70, 500 sq. ft.)– county-name recognizer

Recognizers often have nice characteristics– easy to construct– many off-the-self research & commercial products– applicable across many domains

– help with special cases that are hard to learn

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Meta-Learner: StackingMeta-Learner: Stacking Training of meta-learner produces a weight for every pair of:

– (base-learner, mediated-schema element)

– weight(Name-Learner,address) = 0.1– weight(Naive-Bayes,address) = 0.9

Combining predictions of meta-learner:– computes weighted sum of base-learner confidence scores

<area>Seattle, WA</>(address,0.6)(address,0.8)

Name LearnerNaive Bayes

Meta-Learner (address, 0.6*0.1 + 0.8*0.9 = 0.78)

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Least-SquaresLinear Regression

Training the Meta-LearnerTraining the Meta-Learner

<location> Miami, FL</><listed-price> $250,000</><area> Seattle, WA </><house-addr>Kent, WA</><num-baths>3</>...

Extracted XML Instances Name Learner

0.5 0.8 1 0.4 0.3 0 0.3 0.9 1 0.6 0.8 1 0.3 0.3 0 ... ... ...

Naive Bayes True Predictions

Weight(Name-Learner,address) = 0.1Weight(Naive-Bayes,address) = 0.9

For address

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<extra-info>Beautiful yard</><extra-info>Great beach</><extra-info>Close to Seattle</>

<day-phone>(278) 345 7215</><day-phone>(617) 335 2315</><day-phone>(512) 427 1115</>

<area>Seattle, WA</><area>Kent, WA</><area>Austin, TX</>

Applying the LearnersApplying the Learners

Name LearnerNaive Bayes

Meta-Learner

(address,0.8), (description,0.2)(address,0.6), (description,0.4)(address,0.7), (description,0.3)

(description,0.8), (address,0.2)

Meta-LearnerName LearnerNaive Bayes

(address,0.7), (description,0.3)

(agent-phone,0.9), (description,0.1)

address price agent-phone description

Schema of homes.com Mediated schema

area day-phone extra-info

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The Constraint HandlerThe Constraint Handler

Extends learning to incorporate constraints– hard constraints

– a = address & b = address a = b– a = house-id a is a key– a = agent-info & b = agent-name b is nested in a

– soft constraints– a = agent-phone & b = agent-name

a & b are usually close to each other

– user feedback = hard or soft constraints

Details in [Doan et. al., SIGMOD 2001]

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The Current LSD SystemThe Current LSD System

Mediated schema

Source schemas

Data listings

Constraint Handler

Mappings

User Feedback

Domain Constraints

Matching PhaseTraining Phase

Base-Learner1 Base-Learnerk Meta-Learner

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OutlineOutline Overview of structure mapping

Data integration and source mappings

LSD architecture and details

Experimental results

Current work.

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Empirical EvaluationEmpirical Evaluation

Four domains– Real Estate I & II, Course Offerings, Faculty Listings

For each domain– create mediated DTD & domain constraints– choose five sources– extract & convert data listings into XML (faithful to schema!)– mediated DTDs: 14 - 66 elements, source DTDs: 13 - 48

Ten runs for each experiment - in each run:– manually provide 1-1 mappings for 3 sources– ask LSD to propose mappings for remaining 2 sources– accuracy = % of 1-1 mappings correctly identified

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Matching AccuracyMatching Accuracy

0

10

20

30

40

50

60

70

80

90

100

Real Estate I Real Estate II CourseOfferings

FacultyListings

LSD’s accuracy: 71 - 92%

Best single base learner: 42 - 72%

+ Meta-learner: + 5 - 22%

+ Constraint handler: + 7 - 13%

+ XML learner: + 0.8 - 6%

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Sensitivity to Amount of Available DataSensitivity to Amount of Available Data

40

50

60

70

80

90

100

0 100 200 300 400 500

Ave

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%)

Number of data listings per source (Real Estate I)

30

0

10

20

30

40

50

60

70

80

90

100

Real Estate I Real Estate II Course Offerings Faculty Listings

Contribution of Schema vs. DataContribution of Schema vs. Data

LSD with only schema info.

LSD with only data info.

Complete LSD

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%)

More experiments in the paper [Doan et. al. 01]

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Reasons for Incorrect MatchingReasons for Incorrect Matching

Unfamiliarity – suburb– solution: add a suburb-name recognizer

Insufficient information– correctly identified general type, failed to pinpoint exact type– <agent-name>Richard Smith</>

<phone> (206) 234 5412 </>– solution: add a proximity learner

Subjectivity– house-style = description?

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OutlineOutline Overview of structure mapping

Data integration and source mappings

LSD architecture and details

Experimental results

Current work.

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Moving Up the Expressiveness LadderMoving Up the Expressiveness Ladder

Schemas are very simple ontologies. More expressive power = More domain constraints. Mappings become more complex, but constraints

provide more to learn from. Non 1-1 mappings:

– F1(A1,…,Am) = F2(B1,…,Bm)

Ontologies (of various flavors):– Class hierarchy (I.e., containment on unary relations)– Relationships between objects– Constraints on relationships

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Given two schemas, find– 1-many mappings: address = concat(city,state)– many-1: half-baths + full-baths = num-baths– many-many: concat(addr-line1,addr-line2) = concat(street,city,state)

1-many mappings– expressed as query

– value correspondence expression: room-rate = rate * (1 + tax-rate)

– relationship: state of tax-rate = state of hotel that has rate

– special case: 1-many mappings between two relational tables

Finding Non 1-1 MappingsFinding Non 1-1 MappingsCurrent workCurrent work

address description num-baths

Source schemaMediated schema

city state comments half-baths full-baths

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Brute-Force SolutionBrute-Force Solution

m1, m2, ..., mk

m1

Define a set of operators– concat, +, -, *, /, etc

For each set of mediated-schema columns– enumerate all possible mappings– evaluate & return best mapping

Source-schema columnsMediated-schema columns

compute similarityusing all base learners

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Search-Based SolutionSearch-Based Solution States = columns

– goal state: mediated-schema column– initial states: all source-schema columns

– use 1-1 matching to reduce the set of initial states

Operators: concat, +, -, *, /, etc Column-similarity:

– use all base learners + recognizers

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Multi-Strategy SearchMulti-Strategy Search

Use a set of expert modules: L1, L2, ..., Ln

Each module– applies to only certain types of mediated-schema column– searches a small subspace– uses a cheap similarity measure to compare columns

Example– L1: text; concat; TF/IDF– L2: numeric; +, -, *, /; [Ho et. al. 2000]– L3: address; concat; Naive Bayes

Search techniques– beam search as default– specialized, do not have to materialize columns

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Multi-Strategy Search (cont’d)Multi-Strategy Search (cont’d)

Combine modules’ predictions & select the best one

L1: m11, m12, m13, ..., m1x

L2: m21, m22, m23, ..., m2y

L3: m31, m32, m33, ..., m3z

Apply all applicable expert modules

m11, m12,m21, m22,m31,m32

m11

compute similarityusing all base learners

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Related WorkRelated Work

TRANSCM [Milo&Zohar98]ARTEMIS [Castano&Antonellis99] [Palopoli et. al. 98] CUPID [Madhavan et. al. 01]

SEMINT [Li&Clifton94]ILA [Perkowitz&Etzioni95]DELTA [Clifton et. al. 97]

DELTA [Clifton et. al. 97]

LSD [Doan et. al. 2000, 2001] CLIO [Miller et. al. 00],[Yan et. al. 01]

Single Learner + 1-1 Matching

Hybrid + 1-1 Matching

Schema + Data1-1 + non 1-1 MatchingSophisticated Data-Driven User Interaction

Recognizers + Schema + 1-1 Matching

Multi-Strategy LearningLearners + RecognizersSchema + Data1-1 + non 1-1 Matching

?

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SummarySummary LSD:

– uses multi-strategy learning to semi-automatically generate semantic mappings.

– LSD is extensible and incorporates domain and user knowledge, and previous techniques.

– Experimental results show the approach is very promising.

Future work and issues to ponder:– Accommodating more expressive languages: ontologies– Reuse of learned concepts from related domains.– Semantics?

Data management is a fertile area for Machine Learning research!

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Backup SlidesBackup Slides

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Mapping MaintenanceMapping Maintenance

Source-schema S’Mediated-schema M’

m2

m3

m1

Source-schema SMediated-schema M

m2

m3

m1

Ten months later ...– are the mappings still correct?

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Information Extraction from TextInformation Extraction from Text

Extract data fragments from text documents– date, location, & victim’s name from a news article

Intensive research on free-text documents Many documents do have substantial structure

– XML pages, name card, tables, list

Each such document = a data source– structure forms a schema– only one data value per schema element– “real” data source has many data values per schema element

Ongoing research in the IE community

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Contribution of Each ComponentContribution of Each Component

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40

60

80

100

Real Estate I Course Offerings Faculty Listings Real Estate II

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Without Name Learner

Without Naive Bayes

Without Whirl Learner

Without Constraint Handler

The complete LSD system

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Existing learners flatten out all structures

Developed XML learner– similar to the Naive Bayes learner

– input instance = bag of tokens

– differs in one crucial aspect– consider not only text tokens, but also structure tokens

Exploiting Hierarchical Structure Exploiting Hierarchical Structure

<description> Victorian house with a view. Name your price! To see it, contact Gail Murphy at MAX Realtors.</description>

<contact> <name> Gail Murphy </name> <firm> MAX Realtors </firm></contact>

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Domain ConstraintsDomain Constraints

Impose semantic regularities on sources– verified using schema or data

Examples– a = address & b = address a = b– a = house-id a is a key– a = agent-info & b = agent-name b is nested in a

Can be specified up front– when creating mediated schema– independent of any actual source schema

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area: address contact-phone: agent-phoneextra-info: description

area: address contact-phone: agent-phoneextra-info: address

area: (address,0.7), (description,0.3)contact-phone: (agent-phone,0.9), (description,0.1)extra-info: (address,0.6), (description,0.4)

The Constraint HandlerThe Constraint Handler

Can specify arbitrary constraints User feedback = domain constraint

– ad-id = house-id Extended to handle domain heuristics

– a = agent-phone & b = agent-name a & b are usually close to each other

0.30.10.40.012

0.70.90.60.378

0.70.90.40.252

Domain Constraintsa = address & b = adderss a = b

Predictions from Meta-Learner