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Introducing Semantic Web Basic Lesson -101 120 minutes

Introducing Semantic Web Basic Lesson -101 120 minutes. Organization. Introducing Semantic Web Back-ground, Terminology, definitions vision, Challenges Basics of Building Blocks RDF,OWL, XML Ontology Definitions, Terminology Case Study

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Introducing Semantic Web Basic Lesson -101 120 minutes

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  1. IntroducingSemantic WebBasic Lesson -101 120 minutes Techno Park Bharathidasan University India

  2. Organization • Introducing Semantic Web • Back-ground, Terminology, definitions • vision, Challenges • Basics of Building Blocks • RDF,OWL, XML • Ontology • Definitions, Terminology • Case Study • Uncovered: Ontology, Mining, Algorithms, Coding Syntax Techno Park Bharathidasan University India

  3. WEB • Documents • Standards W3C • Protocols • Connectivity • Frameworks • Tools • Technologies Techno Park Bharathidasan University India

  4. Strength of WEB • Connectivity from disparate document sources / domains • Sophisticated Middleware • Standards • Data Exchange : XML (b2b,b2c,c2c..) Techno Park Bharathidasan University India

  5. WEB – What we expect? • better info-share • more effective info-mgmt • more intelligent search • smarter decision-making • defense capabilities Techno Park Bharathidasan University India

  6. WHAT WE HAVE • Poor Search results • No proper catalogue - leads chaotic web • data management [n-tier] • Unknown size, schema, namespace • Proprietary tools • High interdependency • ex: Are u toxin free? • Health, Environment, • Bio-Chem sites • much manual interaction • Dumb & semi dynamic Techno Park Bharathidasan University India

  7. What should be? • Passive service of delivering docs • Active service of delivering data and concepts wrt context • People, place, inventions, events, companies • Huge Document Retrieval System • Data retrieval system for decision making • Digital Parrot – mimics without understanding • Captures inherent knowledge and advices Techno Park Bharathidasan University India

  8. Evolution Techno Park Bharathidasan University India

  9. Evolution of Semantic/Smarter Web • Web 1.0 • Putting up content in HTML • No interactivity or less (CGI) • Producer puts info – reviewer sees • Mid 90s: Search engines emerge • Web 2.0 • Users modify the content, site web page • Personalization • You tube: create, share, modify • Commercial and Social Sites • Aggregation of SW (Office suite shared/leased) • Blogs, wikis • Remote control Techno Park Bharathidasan University India

  10. Evolution continues • Web 3.0 • Capacity of exhibiting AI • Gives ability to search engines to understand the content by co-relating two or more docs to bring better info to surface • Shallow to deep computing • Ability to initiate actions based on context • Ex context: Back pain: • search, therapy and fix an appointment nearest clinic, earliest date, updating insurance db, updating your calendar, sending CL req to employer Techno Park Bharathidasan University India

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  12. Web, you need help ! • HTML Wrapper Techno Park Bharathidasan University India

  13. Transition • Meta data – 1990 • URL or hyperlink: human can read or guess • But computers, search engines, sw can not • Provide labels and computer readable instruction • Invisible to humans – meta data helps search engines • 1996 US congress: explicit content prevention • XML tags: power to describe and display the document (half semantics) • All are for documents based on URL not for data • Price, title, author of a book Techno Park Bharathidasan University India

  14. Transition • RDF – 2001 officially - W3C spec • Eric Miller pioneered the idea • New Sys for locating and describe data • Classify every bit of online data (pic, text, db, sound..) • Build relationship between classifications • Build dictionaries/thesarus describe same data (captain, price, postal code) • aka Ontology Techno Park Bharathidasan University India

  15. Vision: 3D view of www • Web as single global database • Connecting all data in internet and gives a single window access • DB describes the type of data • (attribute, value) (address, MG Road) • Add concept to the data to make it web data • Whale is an animal • It is mammal, invertebrate, largest Techno Park Bharathidasan University India

  16. SEMANTICS • It is a branch of Linguistics. oops! • Study of meaning. • Relationship between meaning and words. • Syntax: How you say • Punctuation, letters, grammar • Semantics: What you say • Concepts, inherent meaning, values Techno Park Bharathidasan University India

  17. Information Semantics • How it is related to web? • Meaning for our data, systems, documents etc… • Enterprise, Business and Cultural Context • Same concept : double terms (price, cost) • Same term: double meaning (captain) Techno Park Bharathidasan University India

  18. Road to Semantic Web • Is it WEB ++? • An extension of the current web in which information is given well-defined meaning. • W3C • Web will reach its full potential only if its data is shared, processed and understood by automated, independent tools Techno Park Bharathidasan University India

  19. Architects Techno Park Bharathidasan University India

  20. Architects of SW Techno Park Bharathidasan University India

  21. SW Lead, MIT & W3C Techno Park Bharathidasan University India

  22. Definition • Tim BL • Machine Power to instantly access, filter, summarize, bridge and co-relate the data ready to queried • Aggregation of intelligent web sites and Data Stores accessible by semantic technologies, conceptual framework, contracts • Whether it may be search, reasoning, brokering etc. Techno Park Bharathidasan University India

  23. Definitions • Marc Miller • Giving machines the ability of AI to understand the WWW. • Data surfers (aka computer) automatically reason the problem • Stitching complex apps into one screen • Population of AI sw agents to help user make decision • Financial portfolio-bidding, investing Techno Park Bharathidasan University India

  24. Definitions • John Morkoff • Set of Technologies help machines organize the online data and draw conclusions • Daniel Waterhouse • Better way to connect the mass data online and interrogate Techno Park Bharathidasan University India

  25. Terminology • Formal Representation of Info • Like DB • RDF,OWL,SPARQL, DAML+OIL, more coming • It has sensors and effectors in the form of services • Like Robo • Document Web Transforms into Data Web • HTML for Doc Web, RDF for Semantic Web Techno Park Bharathidasan University India

  26. Vision • New Technologies and Services to emerge • All are Semantically aware: • Inference and reputing engines, data-cleansing and thesaurus services, content building tools, authentication • Pure Open Standards, Protocols and Frameworks Techno Park Bharathidasan University India

  27. Vision in Visuals Techno Park Bharathidasan University India

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  30. Trivia • Quiz Techno Park Bharathidasan University India

  31. Building Blocks of SW • 3 components approved by W3C • RDF • XML • OWL • Others • DAML+OIL • SPARQL Techno Park Bharathidasan University India

  32. xTensible • XML • Describe, transport and exchange • Customizable • Provides set of syntax rules for creating semantics in particular domain • Deals with structure of the document • DOM/SAX • Schema/DTD Techno Park Bharathidasan University India

  33. RDF Techno Park Bharathidasan University India

  34. RDF • Underlying language for SW • HTML to Web 2.0, RDF to Semantic Web • Provides infrastructure to link all meta data • Tree like schema • XML driven • Both data composition, transmission • Publishing capability to web Techno Park Bharathidasan University India

  35. RDF • Encodes any info in triples • (like S,V,O) • Every triple is associated with URI • URI identifies the concept of SVO by linking to the original concept • Triples can be converted (serialized) into XML tags to describe the relation between the data • Easy for machine processing Techno Park Bharathidasan University India

  36. RDF Coding //SVO <#pat> <#knows> <#jo> . <#pat> <#age> 24 . //verb "knows" is "property" //a noun expressing a relation between the two. In fact you can write <#pat> <#child> <#al> . //Alternatively both are same: <#pat> has <#child> <#al> . <#al> is <#child> of <#pat> . //SHORT CUT // Same subject. Multiple objects separated by comma //object should be integer or String <#pat> <#child> <#al>, <#chaz>, <#mo> ; <#age> 24 ; <#eyecolor> "blue" . Techno Park Bharathidasan University India

  37. OWL Techno Park Bharathidasan University India

  38. OWL • W3C Web Ontology Language • Logical Language (KR) • Expresses the meaning • Info about individual and Consequences • May work with RDF • As both are logic based • Both uses XML • or independently Techno Park Bharathidasan University India

  39. OWL • Extensions : Sub Languages • DARPA Markup Language DAML (US) • Ontology Inference Language OIL (EU) • Compatible with RDF Schema • May use XML schema, borrows XML syntax • OOP Techno Park Bharathidasan University India

  40. OWL • Represents ontology about a grouping of a person (a concept) • Fits that group into a domain • Based on Classes and properties • Class-object paradigm • Groups ontology info • Stores and transmits on WWW. Techno Park Bharathidasan University India

  41. OWL Vs DB • May work with any DB • DB stores data based on pre-defined schema • OWL stores arbitrary data without schema too • DB is rigid : Table and objects • OWL is flexible and expressive in describing data • DB: data is complete • OWL: may work with incomplete data Techno Park Bharathidasan University India

  42. OWL Coding • Represent family individuals and relations • Code: f:John f:hasWife f:Mary f:John f:hasSon f:Bill f:Mary f:hasSon f:Bill f:John f:hasDaughter f:Susan f:Mary f:hasDaughter f:Susan f:John f:hasAge "33"^^xsd:integer f:Mary f:hasAge "31"^^xsd:integer f:Bill f:hasAge "13"^^xsd:integer f:Susan f:hasAge "8"^^xsd:integer f:John f:hasGender f:male f:Mary f:hasGender f:female f:Bill f:hasGender f:male f:Susan f:hasGender f:female Techno Park Bharathidasan University India

  43. OWL Coding • Owl deals with how family functions • Define a class • Class: f:Person ObjectProperty: f:hasWife domain: f:Person range: f:Person ObjectProperty: f:hasSon domain: f:Person range: f:Person ObjectProperty: f:hasDaughter domain: f:Person range: f:Person DataProperty: f:hasAge domain: f:Person range: xsd:integer • concludes that John belongs to Person • Because the domain of wife is Person and John has a wife. Techno Park Bharathidasan University India

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  45. DAML Philosophy: Tell him something. He combines the new fact with an old one and tell you new thing. But XML? Techno Park Bharathidasan University India

  46. DAML Vs XML • XML gives new thing only with help of embedded software, that too, not in XML standard • But DAML does it – gives back DAML standard • XML does not conclude anything from a XML statement • But DAML does it – concludes from a set of DAML statements Techno Park Bharathidasan University India

  47. DAML • Generic Statements • Mother is a Parent; Mary is the mother of Jesus. • Formal DAML Code • (motherOf subProperty ParentOf) • (Mary motherOf Jesus) • Conclusion: • (Mary ParentOf Jesus) • Result: Mary is the parent of Jesus Techno Park Bharathidasan University India

  48. DAML • subProperty: asserts the 3rd fact from old facts • Property rich language • Multi-lingual Support • ChildOf property in English web • enFanteDe property in French site • (no need to rewrite) • Unique property: iris, finger prints, SSN, PAN, Voter’s ID Techno Park Bharathidasan University India

  49. DAML • Dynamically apply Knowledge from old facts • Infers as humans • Some domains • Finance: All type of accounts of a person (subProperty) (known Person name) • Courier: Rates for Sending mail to unknown city in Europe (known only continent name) Techno Park Bharathidasan University India

  50. SE VISION Techno Park Bharathidasan University India

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