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Web3.0 seminar wipro-session1-pursuitofmeaning
Web3.0 seminar wipro-session1-pursuitofmeaning
Web3.0 seminar wipro-session1-pursuitofmeaning
Web3.0 seminar wipro-session1-pursuitofmeaning
Web3.0 seminar wipro-session1-pursuitofmeaning
Web3.0 seminar wipro-session1-pursuitofmeaning
Web3.0 seminar wipro-session1-pursuitofmeaning
Web3.0 seminar wipro-session1-pursuitofmeaning
Web3.0 seminar wipro-session1-pursuitofmeaning
Web3.0 seminar wipro-session1-pursuitofmeaning
Web3.0 seminar wipro-session1-pursuitofmeaning
Web3.0 seminar wipro-session1-pursuitofmeaning
Web3.0 seminar wipro-session1-pursuitofmeaning
Web3.0 seminar wipro-session1-pursuitofmeaning
Web3.0 seminar wipro-session1-pursuitofmeaning
Web3.0 seminar wipro-session1-pursuitofmeaning
Web3.0 seminar wipro-session1-pursuitofmeaning
Web3.0 seminar wipro-session1-pursuitofmeaning
Web3.0 seminar wipro-session1-pursuitofmeaning
Web3.0 seminar wipro-session1-pursuitofmeaning
Web3.0 seminar wipro-session1-pursuitofmeaning
Web3.0 seminar wipro-session1-pursuitofmeaning
Web3.0 seminar wipro-session1-pursuitofmeaning
Web3.0 seminar wipro-session1-pursuitofmeaning
Web3.0 seminar wipro-session1-pursuitofmeaning
Web3.0 seminar wipro-session1-pursuitofmeaning
Web3.0 seminar wipro-session1-pursuitofmeaning
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Web3.0 seminar wipro-session1-pursuitofmeaning

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  1. 24-06-2010 Web 3.0, Semantics & Session I – Enterprise Computing The Pursuit and Power of Meaning Nagaraju Pappu June 2010 www.canopusconsulting.com2© Canopus Consulting 1
  2. 24-06-2010 What is Web2.0 and  Web2.0 is all about Web3.0? “writing on walls” and “bragging on blogs” True ?3  Web 3.0 is all about “tagging” and “tax-on- omies” False© Canopus Consulting Web2.0 is about Collective Intelligence “The Web isn’t about what you can do with computers. It’s people and, yes, they are connected by computers.4 But computer science, as the study of what happens in a computer, doesn’t tell you about what happens on the Web.” Tim Barness Lee, NY Times, Nov 2, 2006© Canopus Consulting 2
  3. 24-06-2010 What is Collective Intelligence?  intelligent collection?  collaborative bookmarking, searching  “database of intentions”  clicking, rating, tagging, buying what we all know but hadn’t got around to5  saying in public before  blogs, wikis, discussion lists© Canopus Consulting “Collective Knowledge” Systems  “The capacity to provide useful information based on human contributions which gets better as more people participate.  Typically6  mix of structured, machine-readable data and unstructured data from human input© Canopus Consulting 3
  4. 24-06-20107© Canopus Consulting What is agropedia?  Very few useful content related to agriculture on the web (less than 3000 in Wikipedia)  Traditional Knowledge, agricultural knowledge is region and locality specific8  Authentic information is hard to come by – agricultural universities, research, policy, prices, economics, extension community to farming community – the chain is too long  No simple way for the entire community to collaborate, communicate and participate  The semantic distance between the each user community is very huge making any communication virtually impossible!© Canopus Consulting 4
  5. 24-06-20109© Canopus Consulting Experiences of Building Agropedia  The primary challenge is to enable an environment:  which allows the community to grow,10 organize itself,  And, create and organize its own content,  interact and collaborate using the underlying content repository as the primary vehicle of collaboration.© Canopus Consulting 5
  6. 24-06-2010 What is “social” about social computing?  A community is very different from an audience. Audiences can be built, but communities create themselves and grow - but to develop they need an underpinning of a constitution  A way to govern themselves, facilities to create their own languages of communication and interaction and methods to recognize and reward contributions by members.11  When the community becomes too large and too diversified - it loses its focus, the politics of groups would create intrinsic power centers and this would eventually lead to the community falling apart.  The only way to deal with this is to create a platform that would not only be a community network but would also allow formation of networks of communities.  Less of computing – but more “social” problem!© Canopus Consulting Community Vs. User Roles12© Canopus Consulting 6
  7. 24-06-2010 Content as acquired Simple tools or Transformation System APIs, user From experts manual process Tools & user input generated content Editing, language Repository Original Correction Content cross - End user Content linking generated Basic metadata, bookmarks author, source, categories Navigational links Converted to Comments Content processing team for Ontological Standard content entities Format – XML/TeX User defined13 tags Author Publishing Category User rating information Information information or BPO date stamp Discussions Content usage Track backs System Content System at run Statistics, generated indexes rating Content as seen by time an Agropedia user Agropedia Content Transformation Process© Canopus Consulting14© Canopus Consulting 7
  8. 24-06-2010 Web2.0: The function of  Lasting communities Folklore – tags, walls and make up and transmit blogs!! their knowledge, culture True ? and values using folklore15 “folk tradition is ‘folk’ only in respect to its transmission, not its origin. Folklore and Philosophia Perinnis spring from a common source” --Ananda Coomaraswamy What about Content and Content False Organization?© Canopus Consulting16© Canopus Consulting 8
  9. 24-06-2010 PIO University Manipal Teachers Management Dubai Teacher Teacher Distance education Manipal Teacher SMU IT In classroom Medicine Communities interaction Students in a Teacher classroom Students in aIn classroom classroom Students 17interaction Manipal Tech Students in a classroom Student Communities MIT Teacher 1) What teaching tools can I provide? 2) What learning tools can I provide? In classroom interaction Students in a classroom © Canopus Consulting MULN – Business Feature Areas • Author learning material • Across Manipal shared • Workflows for authoring repository of content and publishing • rich semantic indexes and • Control Content Quality common ontology • tools to add user generated content and for licensing, Content rights management and Authoring 18 ownership Environment • Virtual learning communities from institutes to extended classroom Connected •Learning Material, study • Digitally Capture and Content plans and monitor learning complete run of a Repository • Assessment and evaluation, classroom course and grade books transform into useful self • Individual workspaces and learning material Classroom Extended portfolios • Create extended rich- Recording / transform Learning media classrooms Environment Environment © Canopus Consulting 9
  10. 24-06-201019© Canopus Consulting20© Canopus Consulting 10
  11. 24-06-2010 Challenges of Content Repositories  Communities interact and in that process they create valuable information  Content outlasts everything, even its own21 creators – both human as well as technology and tools.© Canopus Consulting Manipal Universal Learning Network  EduNxt brand, distance education…  Large Scale Repositories, Goal Oriented Communities and Thinly distributed expertise  Community of experts, contextualizing content for a goal oriented group22  Agropedia is about community creating and using content – the goal is community enrichment.  MULN is about content being the focus – it is used to increase the quality of teaching and learning.© Canopus Consulting 11
  12. 24-06-2010 The Technology and Engineering Side of the Story…  The Open Sources Revolution and its economic and productivity impact  Dynamic Languages, simple APIs, highly customizable and configurable platforms, large community supported products23 (such as Drupal, MediaWiki etc) have reduced not only the “time to go live”, but also the average programmer salary!!  With good design, one could hire a team of relatively inexperienced programmers and still build a reliable, scalable systems almost on the fly  The transition to Code being the deliverable (and not the application) is a paradigm shift to all parties involved© Canopus Consulting Architecture, Design Challenges  Designing information models that are “application independent”  Design for constitutions and not for “protocols”24  Shift from  Integration to Interoperability  Interoperability to Interaction  Embed the “workflow” in the “content” and not the “data” in the “workflow”  Machine processible “meaning”© Canopus Consulting 12
  13. 24-06-2010Static Equilibrium to Dynamic Harmony True ?25 False© Canopus Consulting Computing & Society – Evolution of Social Applications26© Canopus Consulting 13
  14. 24-06-2010 Role of Technology  capturing everything  storing everything  distributing everything  enabling many-to-many communication27  creating value from the data© Canopus Consulting •Expert Designed Directory Web 1.0 •Cross References (One Url can be at most at 3 places)28 •Storage and linking Web 2.0 are delinked •Only Tags, content is not stored •Community Web Organization of 2.0/3.0 Content© Canopus Consulting 14
  15. 24-06-201029 Social + Semantic Web Social Web© Canopus Consulting Semantics and Ontologies30 Modeling “meaning” for machines© Canopus Consulting 15
  16. 24-06-2010 Web3.0, Ontologies and Agents  Today, “actionable information” requires several tedious “human” steps  For example making a complete travel arrangement (from research, to booking tickets,31 hotels, gathering tourist information, pictures, videos…)  Putting together large amounts of information, and making connections between different pieces of information at each step (making inferences)..© Canopus Consulting Web3.0, Information Interchange Intelligently  Web3.0 seeks to make it possible for automatic agents to interact and interchange information intelligently and without any need for “pre-fabricated” APIs32  Java – Portable Code  XML – Portable Data  RDF(s) – Portable Model  OWL – Portable behavior  Two important aspects:  Why do we need such agents? What can we do with them?  How are Semantic Agents Built?© Canopus Consulting 16
  17. 24-06-2010 Semantics – They Mystery of Meaning  The quest is 5000 years old!  Many approaches, enquiries, investigations and theories  The word for Object in Sanskrit is33 “padArThaM” – literally “the meaning of the word”  The crux of Ontology: “astitva, jñānēyatva, abhidēyatva” “whatever is, is knowable, is namable”© Canopus Consulting Basic stance of ontology is – meanings are entities, events and relations Meanings occur in Cognition Meanings are impressed in cognition & are expressed in natural language34 impress-meanings recur Ontology seeks entitative account of such recurrence Ontological engineering seeks automation of such account Central issue of ontological engineering is – how to specify meaning for robots or computational agents© Canopus Consulting 17
  18. 24-06-2010Formal Vs.Descriptive OntologyFormalOntology isReasoningamong entities35Formal Logic isreasoningamongPropositions© Canopus Consulting Logic of propositions vs. Reasoning among entities  Company is owned by promoters  Company has Employees (Power)  Company has promoters  Company is controlled by the  Company has a management team management team/founders  Company has a board of directors (control)  Managers are employees  Employees are the company  Employees have name, address, (existence)36 role, designation, Salary  Company is engaged in a certain  Company has temporary staff. business operations. (function)  Company has a certain number of  Company needs certain support business units functions (quality)  Company has a certain  Company makes profit (causal) operational, support functions  Company pays taxes  Consultants are associated with the company. (temporal) A Structural Specification A Semantic Specification© Canopus Consulting 18
  19. 24-06-2010 Syntax, Structure and Semantics  Semantics:  Meaning &  Use of Data37© Canopus Consulting 3738US Library of Congress Top Level Hierarchy: • Same Metaphor translated in earlyD: History (general) information systems – File Systems,DA: Great Britain DK: Former Soviet Union Hierarchical DatabasesDB: Austria DL: Scandinavia DP:DC: France Iberian Peninsula DQ:DD: Germany SwitzerlandDE: Mediterranean DR: Balkan Peninsula •Designed to Optimize for Space.DF: Greece DS: AsiaDG: Italy DT: Africa •One Entry can only be at oneDH: Low Countries DU: Oceania placeDJ: Netherlands •Who decides the Categories?© Canopus Consulting 19
  20. 24-06-201039 ? Categories Vs. Tags •Different functions Taxonomies and •Different ways of Folksonomies organizing information, •Different world views© Canopus Consulting Ontology: What can we make of this?40 Meaning in the text Interpretable by common sense© Canopus Consulting 20
  21. 24-06-2010 Ontologies: Data “enriched” with meta-data?41 What about relationships between entities and what they mean?© Canopus Consulting Meaning in the Model (Taxonomy to Ontology – Entities and their Relationships  Capture Parent-Child, Sibling, Spouse relationships  If “X” is a “man” then X can only be “father”, “Son”, “brother” and X cannot be “wife”, “mother”, “sister”  If X is “father” of Y then Y is Son of X  For every male relationship, there is an equivalent female relationship  Father/mother; Husband/Wife; Son/Daughter; Brother/Sister; Nephew/Neice etc..42  Introduce – grand-father, uncle, (grand-mother, aunt), Cousin  Add “in-laws” relationships and their inverse relationships  Add a notion that the relationships “transfer” to the next generation  Machine can “process” the meaning & Machines can “interchange” information and interact with each other  For example, a “paternal” family tree and “maternal” family tree can be merged and conflicts resolved© Canopus Consulting 21
  22. 24-06-2010 Ontology: Reality is in Relationships  Meaning is in Relationships between the entities43  The entity is described, is known, is understood using its relationships to other entities© Canopus Consulting The semantics of computing Ontology Language/ Representation Spectrum Modal Logic First Order Logic Logical Theory Is Disjoint Subclass of Description Logic with transitivity DAML+OIL, OWL, UML property Conceptual Model44 RDFS, XTM Is Subclass of Extended ER Semantic Interoperability Thesaurus ER Has Narrower Meaning Than DB Schemas, XML Schema Structural Interoperability Taxonomy Relational Model Is Sub-Classification of XML Syntactic Interoperability© Canopus Consulting 22
  23. 24-06-2010 The following Sessions will address:  How do we build an application?  How do we build the ontology?  What are the key architecture components?  What are the tools & technologies to use? How do I choose which technology to use?45 © Canopus Consulting Semantic Web Application Lifecycle Ontology Editors: Protégé, TopBraid Composer46 Build Information Model Semantic Query Server Refine/Evolve Information Model Create Assimilation Models & Aggregate knowledge RDF Stores: Mulgara, Sesame Technologies: GRDDL, RDFizers, Programming: Jena OWLs, Automatic Annotation Retrieve and Use Semantic Data© Canopus Consulting 23
  24. 24-06-2010 Semantic Web Application Lifecycle  Information Modelling  Build Ontology (model level representation)  Information Assimilation  Populate Knowledgebase from various sources47  Including current applications  Automatic Semantic Annotation of existing data  Any type of document, multiple sources of documents  Information Retrieval  Applications: search, integrate/portal, summarize/ explain, analyse, decisions support  Reasoning techniques: graph analysis, inferencing© Canopus Consulting Architecture Stack of Semantic Technologies Application HTTP SOAP Programming API Semantic Middleware48 e.g. Semantic SOA SPARQL Processor Inference Engine RDF-SQL Adaptor Relational RDF Store Store Semantic Technology Stack© Canopus Consulting 24
  25. 24-06-2010 Semantic Web Technologies49 Source: W3C© Canopus Consulting The Perceptron.Net Use case  A rich Cultural Informatics environment designed to  Create, Collect, Categorize any type of cultural artifact – Music, Literature, Travel, Leisure,50 Entertainment..  Communities can be formed around content  Make use of existing information on the network and existing community infrastructure  An example:  Indian Music cannot be categorized along the same lines as Western Music  Genre, Album, Artist – is just not sufficient…© Canopus Consulting 25
  26. 24-06-2010 The Perceptron.Net use case…  Typical Queries we want to support:  Thematic Album Creation Ability:  Give me all songs that are directed by X, and music composed by “y” and hero was “z”51  Give me all songs in Raga Kalyani – (must include film, folk and classical songs)  Give me all songs in Lord Rama in Sanskrit, which are “stotras”…  Give me all the recordings of live performacnes at Sri Krishna Gana Sabha, Chennai© Canopus Consulting The Perceptron.Net use case…  Provide an exploratory interface:  Specify a generic criteria and successively filter until you find what you need. E.g: specify a “mood” or a song you like and ask for “similar” songs or songs that match such a mood.  Allow community to add content, meta-data and find new connections in the content.52  Content can be anywhere on the Internet  Raaga.com, HamaraCd.com, MusicToday.Com, Orkut groups, blogs, websites  Not only music, but include content “about” music – articles, essays, ratings, discussions – which should be used in connecting the content, in searching the content, in enriching the content  Provide feeds such that facebook type plug-in can be developed easily – so that content and queries can be shared/updated from anywhere.© Canopus Consulting 26
  27. 24-06-2010 Song/Composition Dimension53© Canopus Consulting 27

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