Alon Halevy

1
Learning to Map Between Schemas Ontologies

• Alon Halevy
• University of Washington
• Joint work with Anhai Doan and Pedro Domingos

2
Agenda

• Ontology mapping is a key problem in many
  applications
• Data integration
• Semantic web
• Knowledge management
• E-commerce
• LSD
• Solution that uses multi-strategy learning.
• Weve 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 Structure Mapping 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 Schemas

• Source schemas XML DTDs

house
address
num-baths
contact-info
agent-name agent-phone
1-1 mapping
non 1-1 mapping
house
location contact
full-baths
half-baths
name phone
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Motivation

• 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 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 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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Outline

• Overview of structure mapping
• Data integration and source mappings
• LSD architecture and details
• Experimental results
• Current work.

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Data Integration
Find houses with four bathrooms priced under
500,000
mediated schema
Query reformulation and optimization.
source schema 2
source schema 3
source schema 1
wrappers
homes.com
realestate.com
homeseekers.com
Applications WWW, enterprises, science
projects Techniques virtual data integration,
warehousing, custom code.
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Semantic Mappings between Schemas

• Source schemas XML DTDs

house
address
num-baths
contact-info
agent-name agent-phone
1-1 mapping
non 1-1 mapping
house
location contact
full-baths
half-baths
name phone
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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.
• P (C1,,Ck) (W) P (A1,,Ak) (R) ,
• P (C1,,Ck) (W) P (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 Difficult

• Aims to identify same real-world entity
• using names, structures, types, data values, etc
• Schemas represent same entity differently
• different names gt same entity
• area address gt location
• same names gt different entities
• area gt 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 Affairs

• Finding semantic mappings is now the bottleneck!
• largely done by hand
• labor intensive error prone
• GTE 4 hours/element for 27,000 elements
  LiClifton00
• 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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Outline

• Overview of structure mapping
• Data integration and source mappings
• LSD architecture and details
• Experimental results
• Current work.

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The 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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Example
Mediated schema
address price agent-phone
description
location listed-price phone
comments
Learned hypotheses
Schema of realestate.com
If phone occurs in the name gt agent-phone
listed-price 250,000 110,000 ...
location Miami, FL Boston, MA ...
phone [phone] [phone] ...
comments Fantastic house Great location ...
realestate.com
If fantastic great occur frequently in
data values gt description
homes.com
price 550,000 320,000 ...
contact-phone [phone] [phone] ...
extra-info Beautiful yard Great beach ...
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Multi-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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Base Learners

• Name Learner

(contact-info,office-address)
(contact-info,office-address)
(contact,agent-phone)
(contact,agent-phone)
(contact-phone, ? )
(phone,agent-phone)
(phone,agent-phone)
(listed-price,price)
(listed-price,price)
contact-phone gt (agent-phone,0.7),
(office-address,0.3)

• Naive Bayes Learner DomingosPazzani 97
• Kent, WA gt (address,0.8), (name,0.2)
• Whirl Learner CohenHirsh 98
• XML Learner
• exploits hierarchical structure of XML data

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Training the Base Learners
Mediated schema
address price agent-phone
description
location listed-price phone
comments
Schema of realestate.com
Name Learner
ltlocationgt Miami, FL lt/gt ltlisted-pricegt
250,000lt/gt ltphonegt [phone]lt/gt
ltcommentsgt Fantastic house lt/gt
(location, address) (listed-price, price) (phone,
agent-phone) ...
realestate.com
Naive Bayes Learner
ltlocationgt Boston, MA lt/gt ltlisted-pricegt
110,000lt/gt ltphonegt [phone]lt/gt
ltcommentsgt Great location lt/gt
(Miami, FL, address) ( 250,000,
price) ([phone], agent-phone) ...
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Entity Recognizers

• Use 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 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

Name Learner Naive Bayes
(address,0.6) (address,0.8)
ltareagtSeattle, WAlt/gt
Meta-Learner
(address, 0.60.1 0.80.9 0.78)
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Training the Meta-Learner

• For address

Name Learner
Naive Bayes
True Predictions
Extracted XML Instances
ltlocationgt Miami, FLlt/gt ltlisted-pricegt
250,000lt/gt ltareagt Seattle, WA lt/gt lthouse-addrgtKen
t, WAlt/gt ltnum-bathsgt3lt/gt ...
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 ...
... ...
Least-SquaresLinear Regression
Weight(Name-Learner,address)
0.1 Weight(Naive-Bayes,address) 0.9
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Applying the Learners
Mediated schema
Schema of homes.com
address price agent-phone
description
area day-phone extra-info
Name Learner Naive Bayes
ltareagtSeattle, WAlt/gt ltareagtKent,
WAlt/gt ltareagtAustin, TXlt/gt
(address,0.8), (description,0.2) (address,0.6),
(description,0.4) (address,0.7), (description,0.3)
Meta-Learner
Name Learner Naive Bayes
Meta-Learner
(address,0.7), (description,0.3)
ltday-phonegt(278) 345 7215lt/gt ltday-phonegt(617) 335
2315lt/gt ltday-phonegt(512) 427 1115lt/gt
(agent-phone,0.9), (description,0.1)
(description,0.8), (address,0.2)
ltextra-infogtBeautiful yardlt/gt ltextra-infogtGreat
beachlt/gt ltextra-infogtClose to Seattlelt/gt
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The 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 System
Matching Phase
Training Phase
Mediated schema
Source schemas
Domain Constraints
Data listings
User Feedback
Constraint Handler
Base-Learner1
Base-Learnerk
Meta-Learner
Mappings
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Outline

• Overview of structure mapping
• Data integration and source mappings
• LSD architecture and details
• Experimental results
• Current work.

27
Empirical 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 Accuracy
Average Matching Acccuracy ()
LSDs 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 Data
Average matching accuracy ()
Number of data listings per source (Real Estate I)
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Contribution of Schema vs. Data
Average matching accuracy ()

• LSD with only schema info.
• LSD with only data info.
• Complete LSD

• More experiments in the paper Doan et. al. 01

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Reasons for Incorrect Matching

• Unfamiliarity
• suburb
• solution add a suburb-name recognizer
• Insufficient information
• correctly identified general type, failed to
  pinpoint exact type
• ltagent-namegtRichard Smithlt/gtltphonegt (206) 234
  5412 lt/gt
• solution add a proximity learner
• Subjectivity
• house-style description?

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Outline

• Overview of structure mapping
• Data integration and source mappings
• LSD architecture and details
• Experimental results
• Current work.

33
Moving 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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Finding Non 1-1 MappingsCurrent work

• 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

Mediated schema
Source schema
address description num-baths
city state comments half-baths full-baths
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Brute-Force Solution

• Define a set of operators
• concat, , -, , /, etc
• For each set of mediated-schema columns
• enumerate all possible mappings
• evaluate return best mapping

Source-schema columns
Mediated-schema columns
compute similarity using all base learners
m1
m1, m2, ..., mk
36
Search-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 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 (contd)

• Apply all applicable expert modules

L1 m11, m12, m13, ..., m1x L2 m21, m22, m23,
..., m2y L3 m31, m32, m33, ..., m3z

• Combine modules predictions select the best one

compute similarity using all base learners
m11
m11, m12, m21, m22, m31,m32
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Related Work
Single Learner 1-1 Matching
Recognizers Schema 1-1 Matching
TRANSCM MiloZohar98 ARTEMIS
CastanoAntonellis99
Palopoli et. al. 98 CUPID Madhavan et. al. 01
SEMINT LiClifton94 ILA PerkowitzEtzioni95 D
ELTA Clifton et. al. 97
Hybrid 1-1 Matching
DELTA Clifton et. al. 97
Multi-Strategy Learning Learners
Recognizers Schema Data 1-1 non 1-1 Matching
Schema Data 1-1 non 1-1 Matching Sophisticated
Data-Driven User Interaction
CLIO Miller et. al. 00,Yan et. al. 01
LSD Doan et. al. 2000, 2001
?
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Summary

• 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 Slides
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Mapping Maintenance
Source-schema S
Mediated-schema M
m1
m2
m3

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

Source-schema S
Mediated-schema M
m1
m2
m3
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Information Extraction from Text

• Extract data fragments from text documents
• date, location, victims 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 Component
Average Matching Acccuracy ()
Without Name Learner Without Naive Bayes Without
Whirl Learner Without Constraint Handler The
complete LSD system
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Exploiting Hierarchical Structure

• 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

ltcontactgt ltnamegt Gail Murphy lt/namegt ltfirmgt
MAX Realtors lt/firmgt lt/contactgt
ltdescriptiongt Victorian house with a view.
Name your price! To see it, contact Gail
Murphy at MAX Realtors. lt/descriptiongt
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Domain 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

47
The Constraint Handler
Domain Constraints a address b adderss
a b
Predictions from Meta-Learner
area (address,0.7),
(description,0.3) contact-phone
(agent-phone,0.9), (description,0.1) extra-info
(address,0.6), (description,0.4)
0.3 0.1 0.4 0.012
area address contact-phone
agent-phone extra-info description
area address contact-phone
agent-phone extra-info address
0.7 0.9 0.6 0.378
0.7 0.9 0.4 0.252

• 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