CS 6999 SWT Lecture 1 Introduction to the

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

Institute for Information Technology l There are two aspects to IIT – – l 2 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 CS 6999 SW Semantic Web Techniques 12 -Sep-02

NRC’s plans for New Brunswick l 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. – – – 3 New Brunswick and Canadian companies already have strengths in all three areas NB’s communications infrastructure and interested telco Bilingual workforce CS 6999 SW Semantic Web Techniques 12 -Sep-02

NRC’s plans for New Brunswick l 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 l Where? – – – 4 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 CS 6999 SW Semantic Web Techniques 12 -Sep-02

Bruce l l MMath 83, BNR 83 -86, Waterloo Ph. D 86 -90, UNB prof 90 -01, NRC 01 -now Automated reasoning – – l 7 data structures in theorem proving eliminate redundant searching smallest proofs deductive databases Java in curriculum since 1997 CS 6999 SW Semantic Web Techniques 12 -Sep-02

Overview and Course Mindmap l l Increasing demand for Namespaces formalized knowledge 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 Web. XQL Horn. ML distributed knowledge Rules Queries XQuery representation Rule. ML Course introduces XML-QL SHOE knowledge markup & resource semantics: Frames RDF[S] Acquisition we show to marry AI representations (e. g. , logics and frames) with Topic. Maps XML & RDF [incl. RDF Protégé Schema] XML l 9 CS 6999 SW Semantic Web Techniques 12 -Sep-02

The Semantic Web Activity of the W 3 C “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. w 3. org/2001/sw/Activity) 10 CS 6999 SW Semantic Web Techniques 12 -Sep-02

What your computer sees in HTML Joe’s Computer Store 365 Yearly Drive Presentation information What your computer sees in XML Joe’s Computer Store

What a computer could understand Joe’s Computer Store 365 Yearly Drive www. canadapost. ca could define address, name, street, … l Search engines could then identify mail addresses l Consider shopbots being able to find l – l 12 price, quantity, feature, model number, supplier, serial number, acquisition date Assumes that namespaces will be used consistently CS 6999 SW Semantic Web Techniques 12 -Sep-02

Semantic Web l Semantics = meaning l 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 l Better – – has hierarchies of terms shades of meaning l Best – 13 idea: Thesaurus idea: Ontology hierarchy of terms and logic conditions CS 6999 SW Semantic Web Techniques 12 -Sep-02

Semantic Web An agent-enabled resource l “information in machine-readable form, creating a revolution in new applications, environments and B 2 B commerce” l W 3 C Activity launched Feb 9, 2001 l DAML: DARPA Agent Markup Language l – – l OIL is Ontology Inference Layer – l 14 US Gov funding to define languages, tools 16 project teams DAML+OIL is joint DARPA-EU Knowledge Representation is a natural choice CS 6999 SW Semantic Web Techniques 12 -Sep-02

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

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

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

• Smoked. Salmon 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 18 Gravalax CS 6999 SW Semantic Web Techniques 12 -Sep-02

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

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

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

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

RDF Example yellow medium model_2300 size medium 23 colour yellow CS 6999 SW Semantic Web Techniques 12 -Sep-02

RDF Class Hierarchy l l All lemon laptops get packed in cardboard boxes Allows one to customize existing taxonomies is_a – Example: palmtop computers still get lemon_palmtop_ 20000 packed in boxes model_2300 size medium 24 CS 6999 SW Semantic Web Techniques colour yellow 12 -Sep-02

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

Ontology Web Language: W 3 C l Previously known as DAML+OIL – – US: DARPA Agent Markup Language EU: Ontology Interchange Layer (Language) l Composed of a hierarchy with additional conditions l Based on Description logic, limited expressivenss – – 26 Reasoning procedures are well-behaved Just enough power CS 6999 SW Semantic Web Techniques 12 -Sep-02

Identifying Resources l URL/URI – – – Uniform resource locator / identifier Information sources, goods and services financial instruments l l “Where do you want to go today? ” – 27 money, options, investments, stocks, etc. becomes “What do you want to find? ” CS 6999 SW Semantic Web Techniques 12 -Sep-02

Ontology l l Branch of philosophy dealing with theory of being Tarski’s assumption: – l “A common vocabulary and agreed-upon meanings to describe a subject domain” – – l What real-world objects do my tags refer to? How are these objects related? Communication requires shared terms – 28 individuals, relationships and functions others can join in CS 6999 SW Semantic Web Techniques 12 -Sep-02

Ontology Layer l Widens interoperability and interconversion – l More meta-information – – l Which attributes are transitive, symmetric Which relations between individuals are 1 -1, 1 -many, many-many Communities exist – – 29 knowledge representation DL, OIL, SHOE (Hendler) New W 3 C working group CS 6999 SW Semantic Web Techniques 12 -Sep-02

Transitive, Subrole example l l l 30 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” is transitive Note that ontologies can be combined at runtime CS 6999 SW Semantic Web Techniques 12 -Sep-02

Combining Rich Ontologies l Only these facts are explicit – l Connected by Acadian Lines in separate ontologies “Connected by bus” – 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 31 CS 6999 SW Semantic Web Techniques 12 -Sep-02

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

Logic Layer l Clausal logic encoded in XML – l Special cases of first-order logic – l – Various implementations: SQL, KIF, SLD (Prolog), XSB J-DREW reasoning tools in Java. Modus operandi: build tractable reasoning systems – 33 Horn Clauses for if-then type reasoning and integrity constraints Standard inference rules based on Resolution – l Rule. ML, IBM Common. Rules trade away expressiveness, gain efficiency CS 6999 SW Semantic Web Techniques 12 -Sep-02

Logic Architecture Example l Contracting parties integrate e-businesses via rules Seller E-Storefront Business Rules Buyer’s Shop. Bot Contract Rules Interchange OPS 5 34 Business Rules Prolog CS 6999 SW Semantic Web Techniques 12 -Sep-02

Negotiation via rules usual. Price: price(per-unit, ? PO, $60) purchase. Order(? PO, supplier. Co, ? Any. Buyer) shipping. Date(? PO, ? D) (? D 24 April 2001). volume. Discount. Price: price(per-unit, ? PO, $55) purchase. Order(? PO, supplier. Co, ? Any. Buyer) quantity. Ordered(? PO, ? Q) (? Q 1000) shipping. Date(? PO, ? D) (? D 24 April 2001). overrides(volume. Discount, usual. Price). 35 CS 6999 SW Semantic Web Techniques 12 -Sep-02

Hot Research Topics: l Tools to create ontologies – – l Tools to learn ontologies from a large corpus such as corporate data – l Merging / aligning two different ontologies from different sources on the same topic Searching cum reasoning tools – 36 Ontolingua Protégé-2000 (Stanford) OILED … SHOE CS 6999 SW Semantic Web Techniques 12 -Sep-02

Eventual Goal of these Efforts l Agents locate goods, services – – – l Gives rise to need of trust, privacy and security – 37 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 CS 6999 SW Semantic Web Techniques 12 -Sep-02