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Is the (Semantic) Web a Database?. Laks V.S. Lakshmanan University of British Columbia http://www.cs.ubc.ca/~laks. Joint work: Igor Naverniouk (UBC) Fereidoon Sadri Univ. of North Carolina @ Greensboro. Thanks to: Wendy Wang Zhimin Chen. Debunking the hype in the title.

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Is the semantic web a database

Is the (Semantic) Web a Database?

Laks V.S. Lakshmanan

University of British Columbia

http://www.cs.ubc.ca/~laks

Joint work:

Igor Naverniouk (UBC)

Fereidoon Sadri

Univ. of North Carolina @ Greensboro

Thanks to:

Wendy Wang

Zhimin Chen


Debunking the hype in the title

Debunking the hype in the title

  • Asilomar 98 Report: the web is a huge DB!…

  • But the web ain’t a DB: Mendelzon, Nov. 98!

  • Our punchline:

    • Adding semantics doesn’t make it a DB!

    • BUT,  a huge embedded collection of repositories of info. (and services) which could greatly benefit from a databasey abstraction

  • Disclaimer:

    • not a talk aboutSW.

    • Work in progress.

SW=DB?, Keynote, IDEAS 2003


Overview

Overview

  • What is the Semantic Web and why bother with it?

  • The Web and Databases

  • SW – technologies & tools

  • The X-DARES-U project @ UBC

  • Summary, Related Work, & Future Challenges

SW=DB?, Keynote, IDEAS 2003


Overview1

Overview

  • What is the Semantic Web and why bother with it?

  • The Web and Databases

  • SW – technologies & tools

  • The X-DARES-U project @ UBC

  • Summary, Related Work, & Future Challenges

SW=DB?, Keynote, IDEAS 2003


Semantic web what and why

Semantic Web – what and why

  • SW = Web + a host of technologies.

    • XML and XML schema

    • Resource Description Framework (RDF) and RDF schema

    • Ontologies (domain specific)

    • Ontology languages (DAML+OIL, OWL, …)

      • Description logics

    • Key idea: semantically mark up your data (and functionality)

    • So, SW = semantic view of the world (web)

SW=DB?, Keynote, IDEAS 2003


Semantic web what and why info discovery

Semantic Web – what and why (Info. discovery)

  • elaborate, precise, automated searches.

e.g.: search program correctly

locates a person based on partial

knowledge:

last name = "Cook," works for a

company on your client list, and

has a son attending your

alma mater, Avondale Univ.

  • semantics will help automate

    complicated processes and

    transactions.

  • Tim Berners- Lee+,

    Sci.Am. May 2001.

SW=DB?, Keynote, IDEAS 2003


Is the semantic web a database

Semantic web – what and why (value chain

creation)

Pete’s agent, Go! Turn down

the volume of Pete’s TV

Pete, how about we taking mom to her physical therapy sessions in turn ?

Lucy

Semantic Web

Sure.

  • Tim Berners- Lee+,

    Sci.Am. May 2001.

Pete’s noisy TV

Pete

SW=DB?, Keynote, IDEAS 2003


Is the semantic web a database

final plan

Lucy’s agent, Go! Retrieve the

related information.

Pete’s Agent, Go!

Give Pete’s schedule to Lucy’s agent.

Address & available

appointment slot

Address & available

appointment slot

Doctor1’s

agent

Doctor2’s

agent

Pete, I will find a clinic within a 20-mile radius of my home and set up the plan for the two of us.

Lucy

Semantic Web

That’s great

  • Tim Berners- Lee+,

    Sci.Am. May 2001.

Pete

SW=DB?, Keynote, IDEAS 2003


Semantic web what and why1

Semantic Web – what and why

“Is this rocket science? Well, not really. The Semantic Web, like the World Wide Web, is just taking well established ideas, and making them work interoperability over the Internet. This is done with standards, which is what the World Wide Web Consortium is all about. We are not inventing relational models for data, or query systems or rule-based systems. We are just webizing them. We are just allowing them to work together in a decentralized system - without a human having to custom handcraft every connection.”

-- Tim Berners-Lee, Business Case for the Semantic Web, http://www.w3.org/DesignIssues/Business

SW=DB?, Keynote, IDEAS 2003


Overview2

Overview

  • What is the Semantic Web and why bother with it?

  • The Web and Databases

  • SW – technologies & tools

  • The X-DARES-U project @ UBC

  • Summary, Related Work, & Future Challenges

SW=DB?, Keynote, IDEAS 2003


The web and databases

The Web and Databases

SW=DB?, Keynote, IDEAS 2003


The web and databases1

The Web and Databases

  • Yet, it’s worthwhile bringing a “databasey”

    look and feel.

  • Semantic web initiative

  • Confluence of knowledge representation, AI,

    IR, DB, …

  • Spell out semantics via semantic markup.

SW=DB?, Keynote, IDEAS 2003


Overview3

Overview

  • What is the Semantic Web and why bother with it?

  • The Web and Databases

  • SW – technologies & tools

  • The X-DARES-U project @ UBC

  • Summary, Related Work, & Future Challenges

SW=DB?, Keynote, IDEAS 2003


Sw technologies tools

SW – Technologies & Tools

Courtesy: Ian Horrocks, CADE 2002.

SW=DB?, Keynote, IDEAS 2003


Sw technologies tools1

SW – Technologies & Tools

  • XML & XML schema

  • RDF & RDF schema

  • Ontologies & Ontology description languages (OWL)

  • SOAP & WSDL [enhance value of SW]

SW=DB?, Keynote, IDEAS 2003


Sw t t xml

SW – T & T (XML)

  • Relevance of XML

    Example:

    <movies>

    <film><fid>F1</fid>

    <title>Manhattan Murder Mystery</title>

    <genre>satire</genre> <genre> mystery</genre>

    <actor><name>woody allen</>

    <role>…</>

    </actor>

    <actor> …

    </film> …

    </movies>

  • No rigid schema, yet self-describing

  • Flexible description/exchange language

  • But, no semantics!

  • “schemaless” ain’t always good!

SW=DB?, Keynote, IDEAS 2003


Sw t t xml schema

SW – T & T (XML schema)

  • No typing and integrity constraints!

  • Fix: DTD (initially) and then XML schema.

  • Example:

    <xs:element name=“film">

    <xs:complexType>

    <xs:sequence>

    <xs:element name=“fid“ type="xs:string“

    minOccurs=“1” maxOccurs=“1”/>

    <xs:element name=“title" type="xs:string“

    min=“1” max=“1”/>

    <xs:element name=“genre" type="xs:string“

    min=“0” max=“unbounded”/> …

    </xs:sequence>

    </xs:complexType>

    </xs:element>

SW=DB?, Keynote, IDEAS 2003


Sw t t rdf

SW – T & T (RDF)

  • XML’s main advantage: near-universal standard for data interchange (e.g., w/ tools to publish from from files, spreadsheets, DB, … sources)

  • Yet, offers no semantics!

    • Your ZIP is my Postal Code

    • Your “name” and my “name” don’t mean the same

  • Besides, XML by itself doesn’t solve info. sharing and interoperability problems

  • Need common unambiguous vocabulary => RDF

SW=DB?, Keynote, IDEAS 2003


Sw t t rdf1

SW – T & T (RDF)

  • Syntax for describing data/resources on the web & relationships in terms of classes and properties

SW=DB?, Keynote, IDEAS 2003


Sw t t rdf2

SW – T & T (RDF)

  • Syntax for describing data/resources on the web & relationships in terms of classes and properties

Example:

http://www.myspace.ca/f1

URI

has_actor

title

http://www.myspace.ca/t1

name

role

A URI can

point to

description of

the resource.

http://www.myspace.ca/f1

...

Woody Allen

Can be URIs too.

SW=DB?, Keynote, IDEAS 2003


Sw t t rdf3

SW – T & T (RDF)

  • classes and resources (subjects): e.g., p1 is a person.

  • properties/predicates map subjects to objects: e.g., p1 has name “Woody Allen”.

  • subjects and predicates – associated with URIs.

  • objects – URIs or literal strings.

  • reification: predicates as resources – e.g.:

    • “domain of name is person”.

  • relationships between classes and between predicates (how to?)

    -- actor is a subclass of person

    -- (predicate) has_actor is a subset of involves

    -- (predicate) directed_by is a subset of involves =>

SW=DB?, Keynote, IDEAS 2003


Sw t t rdf schema

SW – T & T (RDF schema)

  • RDFS – provides RDF vocabulary description and type system.

  • similar to but different from OO languages’ type systems: property-centric vs. class-centric.

  • ontology description languages such as OWL build on it.

  • Example =>

SW=DB?, Keynote, IDEAS 2003


Sw t t rdf schema1

SW – T & T (RDF schema)

<rdf:RDF xml:lang="en"

xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"

xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#">

<rdf:Description ID="registeredTo">

<rdf:type resource="http://www.w3.org/1999/02/22-rdf-

syntax-ns#Property"/>

<rdfs:domain rdf:resource="#MotorVehicle"/>

<rdfs:range rdf:resource="#Person"/>

</rdf:Description>

<rdf:Description ID="rearSeatLegRoom">

<rdf:type resource="http://www.w3.org/1999/02/22-rdf-

syntax-ns#Property"/>

<rdfs:domain rdf:resource="#PassengerVehicle"/>

<rdfs:domain rdf:resource="#Minivan"/>

<rdfs:range rdf:resource="http://www.w3.org/2000/03/example/

classes#Number"/>

</rdf:Description>

</rdf:RDF>

SW=DB?, Keynote, IDEAS 2003


Sw t t owl

SW – T & T (OWL)

  • RDF/RDF schema – too weak to completely describe semantics of application/data.

  • Role filled by languages like DAML+OIL, OWL.

  • E.g., how does an application know watch in one source, wristwatch in another, and clock in a third are closely related?

  • How does it know that curb and kerb are essentially the same thing?

  • More generally, need for relating various terms used in an app. domain and their boolean combos. => ontology.

SW=DB?, Keynote, IDEAS 2003


Sw t t owl1

SW – T & T (OWL)

Example: assume “standard” name spaces. E.g.,

rdf := “http://www.w3.org/1999/02/22-rdf-syntax-ns#”

owl := “http://www.w3.org/2002/07/owl# “

camera :=

“http://www.xfront.com/owl/ontologies/camera#” …

units.domain = Interval.

units.range = Thing.

cost.domain = PrchsbleItem.

cost.range = Money.

shutterspeed.domain = Camera.

shutterspeed.range = Interval.

focal-length = size.

f-stop = aperture.

owl::

Money  Thing.

currency.rdfs:domain = Money.

currency.rdfs:range = Thing.

Interval  Thing.

min.domain = Interval.

min.range = xsd:float.

max.domain = Interval.

max.range = xsd:float.

SW=DB?, Keynote, IDEAS 2003


Sw t t owl2

SW – T & T (OWL)

  • A slightly diff. perspective:

Money[currency->Thing].

Interval[min->float; max->float;

units->Thing].

Camera[shutterspeed->Interval;

size->…; aperture->…].

Type declarations.

Thing

Money

PrchsbleItem

Interval

Camera

Taxonomy.

aperture = f-stop

focal-length = size

Equivalences.

But, terms can refer to

different namespaces.

SW=DB?, Keynote, IDEAS 2003


Sw t t soap

SW – T & T (SOAP)

  • Protocol for exchanging info. over http.

  • Platform & language independent.

  • XML-based.

  • Based on request and response.

    • E.g., getStockPrice: specify stockName and obtain stockPrice.

  • Mandatory & optional functions.

  • Many hops possible between sender and ultimate receiver w/ obligations for intermediate nodes.

  • RPC for web apps. Flexible.

SW=DB?, Keynote, IDEAS 2003


Sw t t wsdl

SW – T & T (WSDL)

  • Distributed computing on the web.

  • Invoke remote method on your data.

  • Invoke remote method on data from some place else.

  • Obtain/provide data (XML).

  • WSDL spec. – XML doc describing service location and operations supported (and types).

  • Used in tandem with SOAP (or other protocols).

  • UDDI – web services registry.

SW=DB?, Keynote, IDEAS 2003


Overview4

Overview

  • What is the Semantic Web and why bother with it?

  • The Web and Databases

  • SW – technologies & tools

  • The X-DARES-U project @ UBC

  • Summary, Related Work, & Future Challenges

SW=DB?, Keynote, IDEAS 2003


The x dares u project at ubc

The X-DARES-U Project at UBC

  • Vision and Goals

  • Architecture

  • XML interoperability

  • Current status

  • Based on [Lakshmanan & Sadri ICSW ’03].

SW=DB?, Keynote, IDEAS 2003


X dares u vision goals

X-DARES-U (vision & goals)

  • XML Data Warehouse with Semantic Enrichment project at UBC.

  • Leverage semantic web to provide interoperability between data sources and services and enable resource discovery. 

  • Enable (partly virtual) warehouse of XML data with support for semantic views.

  • Use hierarchies for flexible, yet powerful data modeling and management.

  • OLAP style analysis and mining functionalities on XML data, leveraging SVs

SW=DB?, Keynote, IDEAS 2003


X dares u architecture

X-DARES-U (architecture)

SW=DB?, Keynote, IDEAS 2003


Is the semantic web a database

Root

Buildings

Animals

People

Animals

Warehouse

Towers

Warehouse

Towers

Directory Server

Global Query

Local Intermediate Results

Local Queries

Source 2

Source 1

Source 3

Source 4

Ontology description in “OWL”

Ontology Server 1

Ontology Server 2

Ontology Server 3

Topic Hierarchy

User query:

For each state, list the warehouse information in that state.

Coordinator

RDF+RDF Schema

Semantic View 1

Semantic View 2

Semantic View 3

Semantic View 4

RDBMS

SW=DB?, Keynote, IDEAS 2003

LDAP

Spreadsheet

XML


Is the semantic web a database

Root

Buildings

Animals

People

Animals

Warehouse

Towers

Warehouse

Towers

Directory Server

Final Results

Inter-source results

Inter-source Queries

Source 2

Source 1

Source 3

Source 4

Ontology description in “OWL”

Ontology Server 1

Ontology Server 2

Ontology Server 3

Topic Hierarchy

User query:

For each state, list the warehouse information in that state.

Coordinator

Global Query

Local Intermediate Results

RDF+RDF Schema

Semantic View 1

Semantic View 2

Semantic View 3

Semantic View 4

RDBMS

SW=DB?, Keynote, IDEAS 2003

LDAP

Spreadsheet

XML


X dares u interoperability

X-DARES-U Interoperability

Here’s how source 1 models its data.

store

*

warehouse

@

*

item

id

city

state

description

id

name

SW=DB?, Keynote, IDEAS 2003


X dares u interoperability1

X-DARES-U Interoperability

Here’s how source 2 models its data.

store

items

warehouses

*

*

item

warehouse

@

id

name

desc.

wid

@

state

id

city

SW=DB?, Keynote, IDEAS 2003


X dares u interoperability2

X-DARES-U Interoperability

Here’s how source 3 models its data.

store

inventory

warehouses

items

*

*

*

i-tuple

w-tuple

inv-tuple

id

name

desc.

id

city

state

i-id

w-id

SW=DB?, Keynote, IDEAS 2003


X dares u interoperability3

X-DARES-U Interoperability

“Find distinct

items available

in warehouses

in each state.”

@!#$%&*()

?

?

?

source1

source3

source2

SW=DB?, Keynote, IDEAS 2003


X dares u interoperability4

X-DARES-U Interoperability

item-id, item-name, item-desc, item-wh,

wh-wid, wh-city, wh-state

source1

source3

source2

SW=DB?, Keynote, IDEAS 2003


X dares u interoperability5

X-DARES-U Interoperability

FOR $S IN distinct(doc(…)/wh-state/tuple/state)

RETURN

<state> {$S}

FOR $X IN doc(…)/wh-state/tuple[state=$S],

$Y IN doc(…)/i-wh/tuple[wh = $X/wh],

$Z IN doc(…)/i-id/tuple[item = $Y/item]

RETURN <item><id> distinct($Z/itemId)}</></></>

wh-state Join

item-wh Join item-id

coordinator

source1

source3

source2

SW=DB?, Keynote, IDEAS 2003


X dares u interoperability6

X-DARES-U Interoperability

  • But who creates these semantic views and how?

  • Who: Local data source administrators.

    • How: we envisage SV authoring tools.

  • Additionally, queries and applications can leverage domain specific ontologies.

  • Several in existence or offing already:

    • camera.owl (www.xfront.com)

    • Dublin core (generic ontology for docs)

    • GPS coordinate, security, space shuttle, … (orlando.drc.com/SemanticWeb/Topics/Ontology/Ontologies.htm)

SW=DB?, Keynote, IDEAS 2003


X dares u interoperability7

X-DARES-U Interoperability

Example – SV authoring for source1

store

*

warehouse

@

*

item

id

city

state

description

id

name

SW=DB?, Keynote, IDEAS 2003


X dares u interoperability8

X-DARES-U Interoperability

Example – SV authoring for source1

store

*

warehouse

@

*

item

id

city

state

description

id

name

SW=DB?, Keynote, IDEAS 2003


X dares u interoperability9

X-DARES-U Interoperability

Example – SV authoring for source1

store

*

warehouse

@

*

item

id

city

state

description

item-wh($I,$W) 

source1/store/warehouse $X,

[email protected] $W, $X/item/id $I

id

name

SW=DB?, Keynote, IDEAS 2003


X dares u interoperability10

X-DARES-U Interoperability

  • Other predicates “populated” similarly: e.g.,

    item-name($I,$N)  source1/store/warehouse/item $X, $X/id $I, $X/name $N

  • Can use URI-generating functions to make it more faithful to RDF spirit:

    • Make all “id”s URIs (standardized)

    • Relate “local” id’s used by source to such URIs

      e.g.: item-id(fI($I),$I)  source1/store/warehouse/item $I

SW=DB?, Keynote, IDEAS 2003


X dares u interoperability11

X-DARES-U Interoperability

  • XML RDF mapping tool:

    • User/admin chooses arguments for RDF predicates

    • “glue” given or inferred (w/ possible user interaction)

    • XSLT mapping program generated automatically

  • BUT, rule-based syntax is more convenient for reasoning.

SW=DB?, Keynote, IDEAS 2003


X dares u local query rewriting

X-DARES-U Local Query Rewriting

  • Global query: p(X,Y) || q(Y,Z)

  • Coordinator handles 2 kinds of queries:

    • Local queries: pi(X,Y) || qi(Y,Z)

    • Inter-source queries: pi(X,Y) || qj(Y,Z)

Global Q

Coordinator

Source Query Rewriter

Src sem. view

Source access code

Ontology

Global Local

SW=DB?, Keynote, IDEAS 2003


X dares u local query rewriting1

X-DARES-U Local Query Rewriting

  • Generation of IS queries – similar.

  • Space of strategy options:

    • Materialize all predicates at coordinator & evaluate locally.

    • Materialize nothing but answers.

    • Partial

    • Choice must be cost-based

    • Dynamic programming approach

    • (Some) inter-source queries eliminable

SW=DB?, Keynote, IDEAS 2003


X dares u local query rewriting2

X-DARES-U Local Query Rewriting

FOR $S IN distinct(doc(…)/wh-state/tuple/state)

RETURN

<state> {$S}

FOR $X IN doc(…)/wh-state/tuple[state=$S],

$Y IN doc(…)/i-wh/tuple[wh = $X/wh],

$Z IN doc(…)/i-id/tuple[item = $Y/item]

RETURN <item><id> {distinct($Z/itemId)}</></></>

source1

store

*

warehouse

@

*

city

state

item

id

id

name

description

SW=DB?, Keynote, IDEAS 2003


X dares u local query rewriting3

X-DARES-U Local Query Rewriting

FOR $S IN source1/store/warehouse/state

RETURN

<state> {$S}

FOR $X IN doc(…)/wh-state/tuple[state=$S],

$Y IN doc(…)/i-wh/tuple[wh = $X/wh],

$Z IN doc(…)/i-id/tuple[item = $Y/item]

RETURN <item><id> {distinct($Z/itemId)}</></></>

source1

store

*

warehouse

@

*

city

state

item

id

id

name

description

SW=DB?, Keynote, IDEAS 2003


X dares u local query rewriting4

X-DARES-U Local Query Rewriting

FOR $S IN source1/store/warehouse/state

RETURN

<state> {$S}

FOR $XG IN source1/store/warehouse[state=$S],

$Y IN doc(…)/i-wh/tuple[wh = $XG/wh],

$Z IN doc(…)/i-id/tuple[item = $Y/item]

RETURN <item><id> {distinct($Z/itemId)}</></></>

source1

store

*

warehouse

@

*

city

state

item

id

id

name

description

SW=DB?, Keynote, IDEAS 2003


X dares u local query rewriting5

X-DARES-U Local Query Rewriting

FOR $S IN source1/store/warehouse/state

RETURN

<state> {$S}

FOR $XG IN source1/store/warehouse[state=$S],

$YG IN source1/store/warehouse[.[email protected][email protected]]

$Z IN doc(…)/i-id/tuple[item = $YG/item]

RETURN <item><id> {distinct($Z/itemId)}</></></>

source1

store

i-id(fI(I), I) 

source1/…/item/id I

*

warehouse

@

*

city

state

item

id

id

name

description

SW=DB?, Keynote, IDEAS 2003


X dares u query optimization

X-DARES-U Query Optimization

FOR $S IN source1/store/warehouse/state

RETURN

<state> {$S}

FOR $XG IN source1/store/warehouse[state=$S],

$YG IN source1/store/warehouse[.[email protected][email protected]]

RETURN <item><id> {distinct($YG/item/id)}</></></>

$XG & $YG are the

same!

source1

store

*

warehouse

@

*

city

state

item

id

id

name

description

SW=DB?, Keynote, IDEAS 2003


X dares u query optimization1

X-DARES-U Query Optimization

FOR $S IN source1/store/warehouse/state

RETURN

<state> {$S}

FOR $XG IN source1/store/warehouse[state=$S]

RETURN <item><id>

{distinct($XG/item/id)}

</></></>

Physical query optimization at source 1

follows this logical optimization.

SW=DB?, Keynote, IDEAS 2003


X dares u query optimization2

X-DARES-U Query Optimization

  • # inter-source queries is O(nm).

  • Good news: some of them can be eliminated.

  • Important even though they are computed in a distributed way.

  • When can we eliminate them?

  • Consider global query: p(X,Y) || q(Y,Z)

SW=DB?, Keynote, IDEAS 2003


X dares u query optimization3

X-DARES-U Query Optimization

  • Consistency condition:

    • Whenever p()p() holds for p, it holds for every fragment.

    • ti pi & tj pj & ti[] = tj[]  ti[] = tj[].

  • Appears too strong at first, but think RDF and URIs.

  • Theorem: Suppose q: YZ. Then inter-source queries involving qi are redundant iff:

    • The consistency condition holds for q.

    • Foreign key constraint: pi[Y]  qi[Y].

SW=DB?, Keynote, IDEAS 2003


X dares u query optimization4

X-DARES-U Query Optimization

  • What if no FDs hold for q(Y,Z)?

  • Weak consistency: the set of Z-values associated with a given Y-value is the same in every qj, where it appears -- q:Y[Z].

  • Theorem: Inter-source queries involving qi are redundant iff:

    • The weak consistency condition holds for q.

    • Referential Integrity constraint:

      pi[Y]  qi[Y].

  • E.g.: y – a person URI and z’s – y’s children.

SW=DB?, Keynote, IDEAS 2003


X dares u query optimization5

X-DARES-U Query Optimization

  • Processing IS queries: pi(X,Y) || qj(Y,Z).

    • Ship doc i to source j & process.

    • Compute pi at source i and ship to source j.

      [who computes/ships what to whom – cost-based dynamic programming].

    • Draw upon distributed QO, but with a twist because of elimination of some IS queries.

    • Additionally, can use semi-antijoin technique in plan space.

    • Suppose p:XY, q:YZ, consistency [or

      just weaker version] hold for both. 

SW=DB?, Keynote, IDEAS 2003


X dares u query optimization6

X-DARES-U Query Optimization

  • r – partial result of p || qcomputed so far.

  • Ship r[X] to source i.

  • Source i: pi’ := {t  pi | t[X] r[X]}.

  • Ship pi’ to source j.

  • Source j: pi’ || qj; updater.

  • r may be a bottleneck: so, maintain local partial results based on what is computed at a source.

SW=DB?, Keynote, IDEAS 2003


X dares u constraint inference

X-DARES-U Constraint Inference

  • Inferring keys: Davidson et al. 2003.

    • Undecidable in general; efficient algorithm under restriction.

  • Focus on foreign keys here:

  • p($X,$Y)  path1 $G, $G/path2 $X, $G/path3 $Y.

    q($W,$Z)  path4 $H, $H/path5 $W, $H/path6 $Z.

    Theorem: Suppose path1/path3 = path4/path5.

    Elements REQUIRED by schema. => Then a RIC

    from pi to qi holds on Y.

SW=DB?, Keynote, IDEAS 2003


X dares u constraint inference1

X-DARES-U Constraint Inference

  • Remarks: undecidability in the general case should not hamper inference in our setting.

    • Mappings highly restrictive.

  • Even hardness of arbitrary XPath query equivalence should not limit us much.

    • Mapping rules generated by tools based on “glue” variables typically “//”-free and/or “*”-free.

SW=DB?, Keynote, IDEAS 2003


X dares u current status

X-DARES-U Current Status

  • Graph-based query language

  • Mapping tool & XMLRDF authoring tool, query optimizer being implemented.

  • Algorithms for further optimization of queries and for constraint inference being developed.

  • Incorporating ontology in query optimization.

  • Warehousing Data model for simultaneous support for multiple hierarchies & transformation from XML.

SW=DB?, Keynote, IDEAS 2003


Overview5

Overview

  • What is the Semantic Web and why bother with it?

  • The Web and Databases

  • SW – technologies & tools

  • The X-DARES-U project @ UBC

  • Summary, Related Work, & Future Challenges

SW=DB?, Keynote, IDEAS 2003


Summary related work future challenges

Summary, Related Work, & Future Challenges

  • The (Semantic) Web is not a DB.

  • But, lots of interesting applications enabled by bringing a DB-ey abstraction to the table.

    • Interoperability

    • Resource discovery

    • Information Integration

    • Analysis & Mining of (Semantic) Web Data

SW=DB?, Keynote, IDEAS 2003


Summary related work future challenges1

Summary, Related Work, & Future Challenges

  • Wealth of work on description logics (dl.kr.org)

  • Dealing with multiple ontologies [Lenzerini et al. 2002-03].

  • Schema Mapping Projects (e.g., Clio [Miller et al. 99+]).

  • Machine learning techniques for ontology mapping (e.g., Halevy et al. 2003).

  • WebBase Project [Garcia-Molina et al. 2002+]

  • Xyleme [Abiteboul et al. 2001+]

  • TAP Project [R.V. Guha et al. 2002].

SW=DB?, Keynote, IDEAS 2003


Summary related work future challenges2

Summary, Related Work, & Future Challenges

  • X-DARES-U Challenges:

  • Interoperability –

    • exploiting ontologies in QO.

    • Constraint Inference: exploit simple special structure of mapping specs.

    • Query enveloping and scheduling.

SW=DB?, Keynote, IDEAS 2003


Summary related work future challenges3

Summary, Related Work, & Future Challenges

  • Supporting multiple hierarchies at once:

    • E.g., authors under books, under geographically grouped institutions, and under a topic hierarchy of research interests.

    • Support powerful ad hoc as well (OLAP-style) analysis queries

  • Support the above over a warehouse without full materialization

  • High level semantic querying of data on the (semantic) web and in the warehouse

SW=DB?, Keynote, IDEAS 2003


Summary related work future challenges4

Summary, Related Work, & Future Challenges

  • X-DARES-U to meet these challenges!

  • Pun is intended!

    Thanks!

    http://www.cs.ubc.ca/~laks.

SW=DB?, Keynote, IDEAS 2003


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