CS6999 SWT Lecture 1 Introduction to the Semantic Web

CS6999 SWTLecture 1

Introduction to the Semantic Web

Bruce Spencer

NRC-IIT Fredericton

Sept 12, 2002

12-Sep-02CS 6999 SW Semantic Web Techniques2

National Research Council

Research Institutes and Facilities across Canada17 research institutes

4 innovation centres

3,500 employees; 1,000 guest workers

National science facilities

S&T information for industry and scientific communityCISTI: Candian Inst. for Science and Tech

InformationNetwork of technology advisors supporting SME

IRAP: Industrial Reseach Assistanceship Program


Institute for Information Technology

There are two aspects to IIT– A mature research organization of ~80 people in Ottawa– New labs being developed in four cities in New

Brunswick and Nova Scotia involving ~60 new people

The whole organization is evolving to accommodate our new distributed nature


NRC’s plans for New Brunswick

What?– NRC is building an e-business research team in New

Brunswick– E-business includes e-learning, e-government, e-health.

Using information and communication technology to help us to educate, govern and take care of ourselves, to create wealth.

– New Brunswick and Canadian companies already have strengths in all three areas

– NB’s communications infrastructure and interested telco– Bilingual workforce


NRC’s plans for New Brunswick

NRC will act locally, and think nationally and globally– Will work with new Brunswick community to develop

clusters in e-business– This is also NRC’s national lab in e-business– NRC will build international links

Where?– Main group (40 staff) in Fredericton, at UNBF– Satellite in Saint John (6 staff), at E-Comm Centre,

UNBSJ– Satellite in Moncton (6 staff), at U. de Moncton


Bruce

MMath 83, BNR 83-86, Waterloo PhD 86-90, UNB prof 90-01, NRC 01-now

Automated reasoning– data structures in theorem proving– eliminate redundant searching– smallest proofs– deductive databases

Java in curriculum since 1997


Overview and Course Mindmap

Increasing demand for formalized knowledge on the Web: AI’s chance!

XML- & RDF-based markup languages provide a 'universal' storage/interchange format for such Web-distributed knowledge representation

Course introduces knowledge markup & resource semantics: we show how to marry AI representations (e.g., logics and frames) with XML & RDF [incl. RDF Schema]

DTDs

XML

RDF[S]

Namespaces

Stylesheets

CSS

XSLT

XQL

Queries

XML-QL

Transformations

Acquisition

Protégé

Agents

Frames

Rules

SHOE

HornML

RuleML

DAML

XQuery

TopicMaps

Ontobroker


The Semantic Web Activityof the W3C

“The Semantic Web is a vision: the idea of havingdata on the Web defined and linked in a way thatit can be used by machines not just for display purposes,but for• automation,• integration and• reuse of data across various applications.”

(http://www.w3.org/2001/sw/Activity)


What your computer sees in HTML

<b>Joe’s Computer Store

</b>

<br>

365 Yearly Drive

What your computer sees in XML<location><name>Joe’s Computer Store</name><address> 365 Yearly Drive</address></location>

Presentation information

Content description

(ambiguous)


What a computer could understand

<mail:address xmlns:mail=“http://www.canadapost.ca”>

<mail:name>Joe’s Computer Store </mail:name>

<mail:street> 365 Yearly Drive </mail:street>

</mail:address>

www.canadapost.ca could define address, name, street, …Search engines could then identify mail addressesConsider shopbots being able to find

– price, quantity, feature, model number, supplier, serial number, acquisition date

Assumes that namespaces will be used consistently


Semantic Web

Semantics = meaningGood Idea: Dictionary

– Create a dictionary of terms– Put it on the web– Mark up web pages so that terms are linked to these

dictionary-entries– This allow more precise matching

Better idea: Thesaurus – has hierarchies of terms– shades of meaning

Best idea: Ontology – hierarchy of terms and logic conditions


Semantic Web

An agent-enabled resource“information in machine-readable form, creating a

revolution in new applications, environments and B2B commerce”

W3C Activity launched Feb 9, 2001DAML: DARPA Agent Markup Language

– US Gov funding to define languages, tools– 16 project teams

OIL is Ontology Inference Layer– DAML+OIL is joint DARPA-EU

Knowledge Representation is a natural choice



•SmokedSalmon is the intersection of Smoked and Salmon

Smoked Salmon


•Gravalax is the intersection of Cured and Salmon, but not Smoked


Smoked Salmon

Gravalax


•Lox is Smoked, Cured Salmon

•Gravalax is the intersection of Cured and Salmon, but not Smoked


Smoked Salmon

Gravalax

Lox


A search for keywords Salmon and Cured should return pages that mention Gravalax, even if they don’t mention Salmon and Cured

A search for Salmon and Smoked will return smoked salmon, should also return Lox, but not Gravalax

Smoked Salmon

LoxGravalax

The Semantic Web is about having the Internet use common sense.


Smoked Salmon

LoxGravalax


Tim Berners- Lee’s Semantic Web


RDF Resource Description Framework

Beginning of Knowledge Representation influence on Web

Akin to Frames, Entity/Relationship diagrams, or Object/Attribute/Value triples


RDF Example

<rdf:ProductSpecs about=

“http://www.lemoncomputers.ca/model_2300”>

<specs:colour>yellow</specs:colour>

<specs:size>medium</specs:size>

</rdf:ProductSpecs>

model_2300

size

medium

colour

yellow


RDF Class Hierarchy

All lemon laptops get packed in cardboard boxes

Allows one to customize existing taxonomies– Example: palmtop

computers still get packed in boxes

lemon_palmtop_20000

is_a

model_2300

size

medium

colour

yellow




Ontology Web Language: W3C

Previously known as DAML+OIL – US: DARPA Agent Markup Language – EU: Ontology Interchange Layer (Language)

Composed of a hierarchy with additional conditions

Based on Description logic, limited expressivenss– Reasoning procedures are well-behaved– Just enough power


Identifying Resources

URL/URI– Uniform resource locator / identifier– Information sources, goods and services– financial instruments

money, options, investments, stocks, etc.

“Where do you want to go today?” – becomes “What do you want to find?”


Ontology

Branch of philosophy dealing with the theory of being Tarski’s assumption:

– individuals, relationships and functions “A common vocabulary and agreed-upon meanings to

describe a subject domain”– What real-world objects do my tags refer to? – How are these objects related?

Communication requires shared terms– others can join in


Ontology Layer

Widens interoperability and interconversion– knowledge representation

More meta-information– Which attributes are transitive, symmetric– Which relations between individuals are 1-1,

1-many, many-many

Communities exist– DL, OIL, SHOE (Hendler)– New W3C working group


Transitive, Subrole example

One wants to ask about modes of transportation from Sydney to Fredericton

“connected by Acadian Lines bus” is a role in a Nova Scotia taxonomy

“connected by SMT bus” from New Brunswick Both are subroles of “connected” “connected” is transitive Note that ontologies can be combined at runtime


Combining Rich Ontologies

Only these facts are explicit– in separate ontologies

“Connected by bus” – is superset– is symmetric and

transitive

Route from Sydney to Fredericton is inferred

Connected by Acadian Lines

Connected by Acadian Lines

Sydney

Truro

Amherst

Fredericton

Connected by SMT Lines

Sussex

Connected by SMT Lines

Amherst




Logic Layer

Clausal logic encoded in XML– RuleML, IBM CommonRules

Special cases of first-order logic– Horn Clauses for if-then type reasoning and integrity

constraintsStandard inference rules based on Resolution

– Various implementations: SQL, KIF, SLD (Prolog), XSB– J-DREW reasoning tools in Java.

Modus operandi: build tractable reasoning systems– trade away expressiveness, gain efficiency


Logic Architecture Example

Contracting parties integrate e-businesses via rules

BusinessRules

BusinessRules

OPS5Prolog

Contract Rules Interchange

Seller E-Storefront Buyer’s ShopBot


Negotiation via rules

usualPrice:

price(per-unit, ?PO, $60) purchaseOrder(?PO, supplierCo, ?AnyBuyer) shippingDate(?PO, ?D) (?D 24April2001).

volumeDiscountPrice:

price(per-unit, ?PO, $55) purchaseOrder(?PO, supplierCo, ?AnyBuyer) quantityOrdered(?PO, ?Q) (?Q 1000) shippingDate(?PO, ?D) (?D 24April2001).

overrides(volumeDiscount, usualPrice).


Hot Research Topics:

Tools to create ontologies– Ontolingua– Protégé-2000 (Stanford)– OILED– …

Tools to learn ontologies from a large corpus such as corporate data– Merging / aligning two different ontologies from different

sources on the same topic

Searching cum reasoning tools– SHOE


Eventual Goal of these Efforts

Agents locate goods, services– use ontologies– unambiguous– business rules– expressive language but reasoning tractable– combine from various sources

Gives rise to need of trust, privacy and security– e.g. semantic web project to determine eligibility of

patients for a clinical trial

Documents

CS6999 SWT Lecture 1 Introduction to the Semantic Web