1. CS6999 SWT Lecture 1 Introduction to the Semantic Web Bruce Spencer NRC-IIT Fredericton Sept 12, 2002

2. 12-Sep-02 CS 6999 SW Semantic Web Techniques 1 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

3. 12-Sep-02 CS 6999 SW Semantic Web Techniques 2 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

4. 12-Sep-02 CS 6999 SW Semantic Web Techniques 3 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

5. 12-Sep-02 CS 6999 SW Semantic Web Techniques 4 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

6. 12-Sep-02 CS 6999 SW Semantic Web Techniques 5 NRC’s 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

7. 12-Sep-02 CS 6999 SW Semantic Web Techniques 6 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.

8. 12-Sep-02 CS 6999 SW Semantic Web Techniques 7 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

9. 12-Sep-02 CS 6999 SW Semantic Web Techniques 8 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

10. 12-Sep-02 CS 6999 SW Semantic Web Techniques 9 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

11. 12-Sep-02 CS 6999 SW Semantic Web Techniques 10 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)http://www.w3.org/2001/sw/Activity

12. 12-Sep-02 CS 6999 SW Semantic Web Techniques 11 What your computer sees in HTML Joe’s Computer Store 365 Yearly Drive What your computer sees in XML Joe’s Computer Store 365 Yearly Drive Presentation information Content description (ambiguous)

13. 12-Sep-02 CS 6999 SW Semantic Web Techniques 12 What a computer could understand Joe’s Computer Store 365 Yearly Drive 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

14. 12-Sep-02 CS 6999 SW Semantic Web Techniques 13 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

15. 12-Sep-02 CS 6999 SW Semantic Web Techniques 14 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 – DAML+OIL is joint DARPA-EU Knowledge Representation is a natural choice

16. 12-Sep-02 CS 6999 SW Semantic Web Techniques 15

17. 12-Sep-02 CS 6999 SW Semantic Web Techniques 16 SmokedSalmon is the intersection of Smoked and Salmon Smoked Salmon

18. 12-Sep-02 CS 6999 SW Semantic Web Techniques 17 Gravalax is the intersection of Cured and Salmon, but not Smoked SmokedSalmon is the intersection of Smoked and Salmon Smoked Salmon Gravalax

19. 12-Sep-02 CS 6999 SW Semantic Web Techniques 18 Lox is Smoked, Cured Salmon Gravalax is the intersection of Cured and Salmon, but not Smoked SmokedSalmon is the intersection of Smoked and Salmon Smoked Salmon Gravalax Lox

20. 12-Sep-02 CS 6999 SW Semantic Web Techniques 19 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 Lox Gravalax The Semantic Web is about having the Internet use common sense.

21. 12-Sep-02 CS 6999 SW Semantic Web Techniques 20 Smoked Salmon Lox Gravalax

22. 12-Sep-02 CS 6999 SW Semantic Web Techniques 21 Tim Berners- Lee’s Semantic Web

23. 12-Sep-02 CS 6999 SW Semantic Web Techniques 22 RDF Resource Description Framework Beginning of Knowledge Representation influence on Web Akin to Frames, Entity/Relationship diagrams, or Object/Attribute/Value triples

24. 12-Sep-02 CS 6999 SW Semantic Web Techniques 23 RDF Example <rdf:ProductSpecs about= “http://www.lemoncomputers.ca/model_2300”> yellow medium model_2300 size medium colour yellow

25. 12-Sep-02 CS 6999 SW Semantic Web Techniques 24 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

26. 12-Sep-02 CS 6999 SW Semantic Web Techniques 25 Tim Berners- Lee’s Semantic Web

27. 12-Sep-02 CS 6999 SW Semantic Web Techniques 26 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

28. 12-Sep-02 CS 6999 SW Semantic Web Techniques 27 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?”

29. 12-Sep-02 CS 6999 SW Semantic Web Techniques 28 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

30. 12-Sep-02 CS 6999 SW Semantic Web Techniques 29 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

31. 12-Sep-02 CS 6999 SW Semantic Web Techniques 30 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

32. 12-Sep-02 CS 6999 SW Semantic Web Techniques 31 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 Sydney Truro Amherst Fredericton Connected by SMT Lines Sussex Connected by SMT Lines Amherst

33. 12-Sep-02 CS 6999 SW Semantic Web Techniques 32 Tim Berners- Lee’s Semantic Web

34. 12-Sep-02 CS 6999 SW Semantic Web Techniques 33 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

35. 12-Sep-02 CS 6999 SW Semantic Web Techniques 34 Logic Architecture Example Contracting parties integrate e-businesses via rules Business Rules Business Rules OPS5 Prolog Contract Rules Interchange Seller E-Storefront Buyer’s ShopBot

36. 12-Sep-02 CS 6999 SW Semantic Web Techniques 35 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).

37. 12-Sep-02 CS 6999 SW Semantic Web Techniques 36 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

38. 12-Sep-02 CS 6999 SW Semantic Web Techniques 37 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