CS6999 SWT Lecture 1 Introduction to the Semantic Web

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CS6999 SWTLecture 1Introduction to the Semantic
Web

• Bruce Spencer
• NRC-IIT Fredericton
• Sept 12, 2002

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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
• ST 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

• 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

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NRCs 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
• NBs communications infrastructure and interested
  telco
• Bilingual workforce

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NRCs 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 NRCs 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

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NRCs plans for New Brunswick

• How much?
• Five year budget 2001-2006
• Fredericton 25.5M
• Saint John 4.5M
• Moncton 4.5M
• Network 3.0M
• (includes link to NBCC Miramichi)
• TOTAL 37.5M

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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 DAMLOIL 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.

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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

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Five main points

• Tim Berners-Lees 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
• Wheres Canada
• Aligns with IIT Frederictons thrusts
• multi-agent, security, OneWeb, voice

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Overview and Course Mindmap

• Increasing demand for formalized knowledge on
  the Web AIs 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

Namespaces
CSS
DTDs
XSLT
Stylesheets
DAML
Agents
Transformations
Ontobroker
XQL
XML
HornML
Rules
Queries
RuleML
XQuery
Mindmap
XML-QL
SHOE
RDFS
Frames
Acquisition
TopicMaps
Protégé
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The Semantic Web Activityof 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)

Semantic Web
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What your computer sees in HTML

• ltbgtJoes Computer Store
• lt/bgt
• ltbrgt
• 365 Yearly Drive

Presentation information
What your computer sees in XML
ltlocationgt ltnamegtJoes Computer
Store lt/namegt ltaddressgt 365 Yearly
Drive lt/addressgt lt/locationgt
Content description (ambiguous)
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What a computer could understand

• ltmailaddress xmlnsmailhttp//www.canadapost.ca
  gt
• ltmailnamegtJoes Computer Store lt/mailnamegt
• ltmailstreetgt 365 Yearly Drive lt/mailstreetgt
• lt/mailaddressgt
• 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

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Semantic Web

• Semantics meaning
• Good 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

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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
• US Gov funding to define languages, tools
• 16 project teams
• OIL is Ontology Inference Layer
• DAMLOIL is joint DARPA-EU
• Knowledge Representation is a natural choice

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• SmokedSalmon is the intersection of Smoked and
  Salmon

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• SmokedSalmon is the intersection of Smoked and
  Salmon

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• SmokedSalmon is the intersection of Smoked and
  Salmon

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

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

• A search for keywords Salmon and Cured should
  return pages that mention Gravalax, even if they
  dont mention Salmon and Cured
• A search for Salmon and Smoked will return smoked
  salmon, should also return Lox, but not Gravalax

Smoked Salmon
Lox
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Smoked Salmon
Lox
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Tim Berners- Lees Semantic Web
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RDF Resource Description Framework

• Beginning of Knowledge Representation influence
  on Web
• Akin to Frames, Entity/Relationship diagrams, or
  Object/Attribute/Value triples

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RDF Example

• ltrdfProductSpecs about
• http//www.lemoncomputers.ca/model_2300gt
• ltspecscolourgtyellowlt/specscolourgt
• ltspecssizegtmediumlt/specssizegt
• lt/rdfProductSpecsgt

model_2300
size
colour
medium
yellow
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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

model_2300
size
colour
medium
yellow
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Tim Berners- Lees Semantic Web
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Ontology Web Language W3C

• Previously known as DAMLOIL
• 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

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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?

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Ontology

• Branch of philosophy dealing with the theory of
  being
• Tarskis 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

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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

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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

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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

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Tim Berners- Lees Semantic Web
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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
• J-DREW reasoning tools in Java.
• Modus operandi build tractable reasoning systems
• trade away expressiveness, gain efficiency

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Logic Architecture Example

• Contracting parties integrate e-businesses via
  rules

Seller E-Storefront
Buyers ShopBot
Business Rules
Business Rules
Contract Rules Interchange
OPS5
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

• 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

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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