Alon Halevy

University of Washington

Joint work with Anhai Doan, Jayant Madhavan,

Phil Bernstein, and Pedro Domingos

Peer Data-Management Systems:Peer Data-Management Systems:Plumbing for the Semantic WebPlumbing for the Semantic Web

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AgendaAgenda

Elements of the Semantic Web Piazza: a peer data-management system

– A database guy’s contribution to the semantic web The key issue: mapping between different models:

– Some recent progress and current directions. The critical issue: crossing the structure chasm.

The talk I’m not giving today:– A critique of the Semantic Web.

Work and thoughts are in progress

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The Semantic Web (my view)The Semantic Web (my view) Web sites include structural annotations

– You can pose meaningful queries on them.– Ontologies provide the semantic glue.– Internal implementation of web sites left open.

Agents perform tasks:– Query one or more web sites– Perform updates (e.g., set schedules)– Coordinate actions– Trust each other (or not).

I.e., agents operating on a gigantic heterogeneous distributed database.

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Getting thereGetting there

Robust infrastructure for querying – Peer data management systems.

Facilitate mapping between different structures. Need tools for: – Locating relevant structures– Easily joining the semantic web.

Get data into structured form– Should we worry about the legacy web?

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AgendaAgenda

Elements of the Semantic Web (personal view) Piazza: a peer data-management system

– A database guy’s contribution to the semantic web

The key issue: mapping between different models: – Some recent progress and current directions.

The critical issue: crossing the structure chasm.

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Piazza: Peer Data-ManagementPiazza: Peer Data-Management

Goal: To enable users to share data across local or wide area networks in an ad-hoc, highly dynamic

distributed architecture.

Peers can:– Export base data– Provide views on base data– Serve as logical mediators for other peers

Every peer can be both a server and a client. Peers join and leave the PDMS at will.

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Extending the Vision to Data SharingExtending the Vision to Data Sharing

911 DispatchCenter (9DC)

FireServices (FS)

PortlandFire District (PFD)

Vancouver FireDistrict (VFD)

Station 12Station 19Station 3 Station 32

FirstHospital

(FH)Hospitals

(H)

LakeviewHospital (LH)

MedicalAid (MA)

EarthquakeCommand

Center (ECC)

Search &Rescue (SR)

EmergencyWorkers (EW)

WashingtonState

NationalGuard

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Relationship of PDMS to…Relationship of PDMS to…

P2P overlay networks (the “S” word) Data integration systems (no central logical

mediated schema) Federated databases (scale, ad-hoc nature) Distributed databases (no central administration)

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Representing DataRepresenting Data A spectrum of possibilities:

– Relational tables, some integrity constraints– XML: can encode relational, hierarchical, OO

– Xquery – emerging standard query language (SQL for XML)

– RDF: “XML on drugs”.– Sees only the logic; ignores other aspects.

– DAML+OIL– Full blown Knowledge representation language.

They all have semantics; just different expressive powers.

We keep the data simple. Mappings between data at different peers are more complex.

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Piazza QueryingPiazza Querying Semantic mappings between peers provide glue:

LH:CritBed(bed, hosp, room, PID, status) H:CritBed(bed, hosp, room) & H:Patient(PID, bed, status)

9DC:SkilledPerson(PID, "Doctor") :- H:Doctor(SID, h, l, s, e)9DC:SkilledPerson(PID, "EMT") :- H:EMT(SID, h, vid, s, e)

Query processing phases:– Reformulate a query into queries over stored data.

– Minicon algorithm (++) for answering queries using views.– Extensions in Piazza enable chaining multiple peer mappings.

– Find best plan for the query and execute it:– Tukwila data integration engine – an efficient processor for

network bound XML/relational data.

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Efficiency Issues in Piazza Efficiency Issues in Piazza Intelligent data placement:

– We may want to place views over data at key points in the PDMS:

– Save work for frequently asked queries.– Increase availability in cases of failures.

– Akamai for structured data– A form of automated reformulation.– Large search space of possibilities– Surprising lower bounds on very simple cases [Chirkova

et al, VLDB 2001].

Efficient propagation of updates:– Approach: publish updategrams as first-class citizens.

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Additional Piazza IssuesAdditional Piazza Issues

The catalog of data sources– What does a catalog of structured data sources look like?– How can it be browsed by humans?– How do we facilitate joining a PDMS?– How can the catalog be distributed physically?

Systems issues:– Architecture of a Piazza node: what are the components?– Naming issues– Security

Piazza collaborators: Etzioni,Gribble, Ives, Levy, Suciu, Mork, Rodrig, Tatarinov.

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AgendaAgenda

Elements of the Semantic Web Piazza: a peer data-management system

– A database guy’s contribution to the semantic web

The key issue: mapping between different models: – Some recent progress and current directions.

The critical issue: crossing the structure chasm.

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It’s All About the MappingsIt’s All About the MappingsIt’s not about understanding the data: It’s about understanding each other.

Whenever you see a model for some domain, there is another one hiding around the corner.

Mappings provide semantic relationships between different peers.

Specifying mappings: inherently a human-assisted task.

Goal: make it easy, fast, incremental. Not a new problem!

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Example Semantic MappingExample Semantic Mapping Mapping between 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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Desiderata from Proposed SolutionsDesiderata from Proposed Solutions

Accuracy, efficiency, ease of use. Extensible: accommodate in a principled fashion:

– User feedback– Domain constraints– General heuristics

“Memory”, knowledge reuse:– System should exploit knowledge from previous matching

tasks [LSD].

Some underlying semantics.

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Why Matching is DifficultWhy Matching is Difficult Structures represent same entity differently

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

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

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

Schema, data and rules never fully capture semantics!– not adequately documented, certainly not for machine

consumption.

Often hard for humans (committees are formed!)

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Learning for MappingLearning for Mapping We started simple: generating semantic mappings

between a 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!) L(earning) S(ource) D(escriptions) [SIGMOD 2001]. Recent and current work:

– (simple) Ontology mapping [WWW-02]– Complex mappings [COMAP]– Semantics [Madhavan et al., AAAI-02]

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Data Integration (a simple PDMS)Data Integration (a simple PDMS)

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.

Query reformulationand optimization.

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price agent-name agent-phone office-phone description

Learning from the Manual Mappings Learning from the Manual Mappings

listed-price contact-name contact-phone office comments

Schema of realestate.com

Mediated schema

$250K James Smith (305) 729 0831 (305) 616 1822 Fantastic house $320K Mike Doan (617) 253 1429 (617) 112 2315 Great location

listed-price contact-name contact-phone office comments

realestate.com

If “fantastic” & “great” occur frequently in data instances => descriptionsold-at contact-agent extra-info

$350K (206) 634 9435 Beautiful yard $230K (617) 335 4243 Close to Seattle $190K (512) 342 1263 Great lot

homes.com

If “office” occurs in the name => office-phone

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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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Base LearnersBase Learners Training

Matching Name Learner

– training: (“location”, address) (“contact name”, name)

– matching: agent-name => (name,0.7),(phone,0.3) Naive Bayes Learner

– training: (“Seattle, WA”,address) (“250K”,price)matching: “Kent, WA” => (address,0.8),(name,0.2)

labels weighted by confidence scoreX

(X1,C1)(X2,C2)...(Xm,Cm)

Observed label

Training examples

Object

Classification model (hypothesis)

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Meta-Learner: StackingMeta-Learner: Stacking[Wolpert 92,Ting&Witten99][Wolpert 92,Ting&Witten99]

Training– uses training data to learn weights– one for each (base-learner,mediated-schema element) pair– weight (Name-Learner,address) = 0.2– weight (Naive-Bayes,address) = 0.8

Matching: combine predictions of base learners– computes weighted average of base-learner confidence scores

Seattle, WAKent, WABend, OR

(address,0.4)(address,0.9)

Name LearnerNaive Bayes

Meta-Learner (address, 0.4*0.2 + 0.9*0.8 = 0.8)

area

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The LSD ArchitectureThe LSD ArchitectureMatching PhaseTraining Phase

Mediated schemaSource schemas

Base-Learner1 Base-Learnerk

Meta-Learner

Training datafor base learners

Hypothesis1 Hypothesisk

Weights for Base Learners

Base-Learner1 .... Base-Learnerk

Meta-Learner

Prediction Combiner

Predictions for elements

Predictions for instances

Constraint Handler

Mappings

Domainconstraints

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Domain ConstraintsDomain Constraints

Encode user knowledge about the domain Specified by examining mediated schema Examples

– at most one source-schema element can match address– if a source-schema element matches house-id then it is a key– avg-value(price) > avg-value(num-baths)

Given a mapping combination – can verify if it satisfies a given constraint

area: addresssold-at: price contact-agent: agent-phoneextra-info: address

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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

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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

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0 100 200 300 400 500

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Number of data listings per source (Real Estate I)

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0

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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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Contribution of Each ComponentContribution of Each Component

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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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The Next Steps The Next Steps Learning is a useful component. But it needs to be

combined with:– User feedback– Domain constraints– General heuristics

Need a representation of mappings:– First step – see [Madhavan et al., AAAI-02]

– Also defines key inference problems for such a representation,– Provides answers for the mapping language used in Piazza.

– Ultimately, some first-order probabilistic representation.

Need benchmarks to measure progress.

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AgendaAgenda

Elements of the Semantic Web Piazza: a peer data-management system

– A database guy’s contribution to the semantic web

The key issue: mapping between different models: – Some recent progress and current directions.

The critical issue: crossing the structure chasm.

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Can We Cross the Structure Chasm? Can We Cross the Structure Chasm? There are two worlds:

– U-world: the current web, keyword search, google– S-world: databases, knowledge bases, structured queries

The web succeeded because it’s in the u-world. For the semantic web to succeed, we need to make it dead

simple for people to:– Structure data, locate relevant data and data sets, query.

However:– People have a hard time structuring their data– It’s harder to query structured data: need to know a terminology.– It’s harder to understand each other in the S-world.

DB and KR people have no clue how to deal with this. More expressive power in the languages won’t help.