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Abstract Much of the AI community that met at IJCAI in August 2001 was discussing the
"Semantic Web", a proposal by the inventor of the web, Tim Berners-Lee, and others to adding meaning to terms for items found on the web, with a view to making the web interactions more accurate and more easily automated. Several US and European projects are concerned with creating and using taxonomies of terms in web page design and retrieval, and are supported by W3C and DARPA. The DAML+OIL language, a joint US-European project, proposes to add Resource Description Framework (RDF) to Extensible Markup Language (XML), tagging web content with meta-tags containing links to ontologies, as well as facts and rules that describe the intended use of the content. This draws from a quarter century of work in knowledge representation and reasoning systems by the artificial intelligence community.
In this talk I will explain the goals and achievements of the Semantic Web effort to date, and point out (some of) the remaining hurdles, and assuming that they are cleared, what these researchers expect to emerge. Interoperation among applications that exchange machine-understandable information will allow automated processing of web resources, and this has many applications in ecommerce. I will close with a suggestion how the IIT-Fredericton's Security/Privacy, Multi-Agent and "One Web" thrusts can be aligned with these international efforts.
Five main points
• Tim Berners-Lee’s vision–web information should be machine
understandable• Taxonomies of words shared within web
communities– no single ontology
• RDF: meta-tags link XML tags to their roles• US and European buy-in–Where’s Canada
• Aligns with IIT Fredericton’s thrusts–multi-agent, security, OneWeb, voice
Identifying Resources
• URL/URI– Uniform resource locator / indicator– 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 metaphysics dealing with the
philosophical theory of reality• Tarski: individuals, relationships and roles• “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
What your computer sees in HTML<b>Joe’s Computer Store</b><br> 365 Yearly Drive
What your computer sees in XML<location><address><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 addresses• Consider shopbots being able to find – price, quantity, feature, model number, supplier, serial
number, acquisition date• Assumes that namespaces will be used consistently
Semantic Web
• An agent-enabled resource• “information in machine-readable form, creating
a revolution in new applications, environments and B2B commerce”
• Launched Feb 9, 2001• DAML: 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
• KR a natural choice
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 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 constraints• Standard inference rules based on Resolution– Various implementations: SQL, KIF, SLD
(Prolog), XSB– I’ve developed reasoning tools in Java.
• Modus operandi: build tractable reasoning systems– trade some expressiveness
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).
Eventual Goal of these Efforts
• Agents locate goods, services– use namespaces, ontologies– unambiguous– business rules– expressive language but reasoning tractable– combine from various sources
• Gives rise to needs trust, privacy and security– e.g. semantic web project to determine
eligibility of patients for a clinical trial
Aligning with IIT’s identified thrusts
• Privacy– Tim Berners-Lee’s “Web of Trust” digital
signatures• Multi Agents Systems• One Web– high degree of interoperability– shared ontologies need to be multilingual and
multicultural
What could IIT-Fredericton do?
• W3C working group on Semantic web ontologoes announced Aug 13– request for participants–We need to be poised to adopt emerging
standards.• Build prototypes–with an industrial partner• resource location (goods and services)• procurement• markup-for-free• match ontologies
What could IIT-Fredericton do?
• International partners: – DFKI (Germany) Harold Boley– Rutherford Appleton (UK) Theo Dimitrakos
Easy reading“Agents and the Semantic Web” IEEE Intelligent Systems
Journal March/April 2001, James Hendler. “The Semantic Web” Scientific American, June 2001, Tim
Berners-Lee, James Hendler, Ora Lassila.