CS6999 SWT
Lecture 1
Introduction to the Semantic Web
Bruce Spencer
NRC-IIT Fredericton
Sept 12, 2002
National Research Council
Research Institutes and Facilities across Canada
17 research institutes
4 innovation centres
3,500 employees; 1,000 guest workers
National science facilities
S&T information for industry and scientific community
CISTI: Candian Inst. for Science and Tech
Information
Network of technology advisors supporting SME
IRAP: Industrial Reseach Assistanceship Program
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Institute for Information Technology
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There are two aspects to IIT
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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
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NRC’s plans for New Brunswick
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What?
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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.
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New Brunswick and Canadian companies already have
strengths in all three areas
NB’s communications infrastructure and interested telco
Bilingual workforce
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NRC’s plans for New Brunswick
 NRC
will act locally, and think nationally and
globally
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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?
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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
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Bruce
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MMath 83, BNR 83-86, Waterloo PhD 86-90, UNB
prof 90-01, NRC 01-now
Automated reasoning
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data structures in theorem proving
eliminate redundant searching
smallest proofs
deductive databases
Java in curriculum since 1997
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Overview and Course Mindmap
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Increasing demand for
formalized knowledge
Namespaces
on the Web: AI’s
CSS
DTDs
chance!
XSLT
XML- & RDF-based
DAML
Stylesheets
markup languages
Agents
provide a 'universal'
Transformations
Ontobroker
storage/interchange
format for such WebXQL
HornML
distributed knowledge
Rules
Queries
representation
XQuery
RuleML
Course introduces
XML-QL
SHOE
knowledge markup &
resource semantics:
Frames RDF[S] Acquisition
we show how to marry
AI representations (e.g.,
logics and frames) with
TopicMaps
XML & RDF [incl. RDF
Protégé
Schema]
XML
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The Semantic Web Activity
of the W3C
“The Semantic Web is a vision: the idea of having
data on the Web defined and linked in a way that
it 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)
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What your computer sees in HTML
<b>Joe’s Computer Store
</b>
<br>
365 Yearly Drive
Presentation
information
What your computer sees in XML
<location>
<name>Joe’s Computer Store
</name>
<address> 365 Yearly Drive
</address>
</location>
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Content
description
(ambiguous)
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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 addresses
 Consider shopbots being able to find
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price, quantity, feature, model number, supplier, serial number,
acquisition date
Assumes that namespaces will be used consistently
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Semantic Web
 Semantics
= meaning
 Good Idea: Dictionary
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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
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has hierarchies of terms
shades of meaning
 Best
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idea: Thesaurus
idea: Ontology
hierarchy of terms and logic conditions
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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, 2001
 DAML: DARPA Agent Markup Language
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OIL is Ontology Inference Layer
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US Gov funding to define languages, tools
16 project teams
DAML+OIL is joint DARPA-EU
Knowledge Representation is a natural choice
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•SmokedSalmon is the intersection
of Smoked and Salmon
Smoked Salmon
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•SmokedSalmon is the intersection
of Smoked and Salmon
Smoked Salmon
•Gravalax is the intersection of
Cured and Salmon, but not
Smoked
Gravalax
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•SmokedSalmon is the intersection
of Smoked and Salmon
Smoked Salmon
•Gravalax is the intersection of
Cured and Salmon, but not
Smoked
Lox
•Lox is Smoked, Cured Salmon
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Gravalax
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The Semantic Web is about having the Internet use
common sense.
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A search for keywords
Salmon and Cured
should return pages that
mention Gravalax, even if
they don’t mention
Salmon and Cured
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A search for Salmon and
Smoked will return
smoked salmon, should
also return Lox, but not
Gravalax
Smoked Salmon
Lox
Gravalax
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Smoked Salmon
Lox
Gravalax
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Tim Berners- Lee’s Semantic Web
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RDF Resource Description Framework
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Beginning of Knowledge Representation influence
on Web
Akin to Frames, Entity/Relationship diagrams, or
Object/Attribute/Value triples
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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
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colour
yellow
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RDF Class Hierarchy
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All lemon laptops get
packed in cardboard
boxes
Allows one to
customize existing
taxonomies
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Example: palmtop
computers still get
packed in boxes
is_a
lemon_palmtop_
20000
model_2300
size
medium
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colour
yellow
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Tim Berners- Lee’s Semantic Web
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Ontology Web Language: W3C
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Previously known as DAML+OIL
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US: DARPA Agent Markup Language
EU: Ontology Interchange Layer (Language)
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Composed of a hierarchy with additional conditions
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Based on Description logic, limited expressivenss
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Reasoning procedures are well-behaved
Just enough power
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Identifying Resources
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URL/URI
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Uniform resource locator / identifier
Information sources, goods and services
financial instruments
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“Where do you want to go today?”
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money, options, investments, stocks, etc.
becomes “What do you want to find?”
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Ontology
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Branch of philosophy dealing with the theory of being
Tarski’s assumption:
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“A common vocabulary and agreed-upon meanings to
describe a subject domain”
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What real-world objects do my tags refer to?
How are these objects related?
Communication requires shared terms
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individuals, relationships and functions
others can join in
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Ontology Layer
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Widens interoperability and interconversion
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More meta-information
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Which attributes are transitive, symmetric
Which relations between individuals are 1-1,
1-many, many-many
Communities exist
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knowledge representation
DL, OIL, SHOE (Hendler)
New W3C working group
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Transitive, Subrole example
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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
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Combining Rich Ontologies
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Only these facts are
explicit
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Connected by Acadian Lines
in separate ontologies
“Connected by bus”
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Amherst
is superset
is symmetric and
transitive
Amherst
Truro
Connected by Acadian Lines
Sydney
Route from Sydney to
Fredericton is inferred
Connected by SMT Lines
Sussex
Connected by SMT Lines
Fredericton
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Tim Berners- Lee’s Semantic Web
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Logic Layer
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Clausal logic encoded in XML
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Special cases of first-order logic
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Various implementations: SQL, KIF, SLD (Prolog), XSB
J-DREW reasoning tools in Java.
Modus operandi: build tractable reasoning systems
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Horn Clauses for if-then type reasoning and integrity
constraints
Standard inference rules based on Resolution
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RuleML, IBM CommonRules
trade away expressiveness, gain efficiency
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Logic Architecture Example
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Contracting parties integrate e-businesses via
rules
Seller E-Storefront
Business
Rules
Buyer’s ShopBot
Contract Rules
Interchange
OPS5
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Business
Rules
Prolog
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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).
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Hot Research Topics:
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Tools to create ontologies
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Tools to learn ontologies from a large corpus such
as corporate data
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Merging / aligning two different ontologies from different
sources on the same topic
Searching cum reasoning tools
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Ontolingua
Protégé-2000 (Stanford)
OILED
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SHOE
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Eventual Goal of these Efforts
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Agents locate goods, services
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Gives rise to need of trust, privacy and security
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use ontologies
unambiguous
business rules
expressive language but reasoning tractable
combine from various sources
e.g. semantic web project to determine eligibility of
patients for a clinical trial
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