Web3.0 seminar wipro-session1-pursuitofmeaning
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Transcript of Web3.0 seminar wipro-session1-pursuitofmeaning
24-06-2010
1
Session I – The Pursuit and Power of
Meaning
Nagaraju Pappu
June 2010
Web 3.0, Semantics &
Enterprise Computing
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What is Web2.0 and Web3.0?
Web2.0 is all about “writing on walls” and “bragging on blogs”
Web 3.0 is all about “tagging” and “tax-on-omies” ?
True
False
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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. 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
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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 to saying in public before
blogs, wikis, discussion lists
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“Collective Knowledge” Systems “The capacity to provide useful information
based on human contributions which gets better as more people participate.
Typically
mix of structured, machine-readable data and unstructured data from human input
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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 specific
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!
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Experiences of Building Agropedia
The primary challenge is to enable an environment: which allows the community to grow,
organize itself,
And, create and organize its own content,
interact and collaborate using the underlying content repository as the primary vehicle of collaboration.
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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.
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!
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Community Vs. User Roles
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Content as acquired
From experts
Converted to
Standard content
Format – XML/TeX
Editing, language
Correction
Navigational links
for Ontological
entities
Author
information
Content cross -
linking
Publishing
Information
date stamp
Category
information
Content usage
Statistics,
End user
generated
bookmarks
Comments
User defined
tags
Track backs
User rating
Basic metadata,
author, source,
categories
Original
Content
Reposi
tory
Conte
nt
pro
cess
ing
team
or
BPO
Simple tools or
manual process
Transformation
Tools & user input
System APIs, user
generated content
System
generated
rating
Content
indexes
Syst
em
at
run
tim
e
Content as seen by
an Agropedia user
Discussions
Agropedia Content Transformation Process
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Web2.0: The function of Folklore – tags, walls and blogs!!
Lasting communities make up and transmit their knowledge, culture and values using folklore
“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
Organization?
? True
False
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Teacher
Students in a
classroom
PIO University
In classroom
interaction
Teacher
Students in a
classroom
Dubai
In classroom
interaction
Teacher
Students in a
classroom
MIT
In classroom
interaction
Teacher
Students in a
classroom
SMU IT Teacher
Communities
Student
Communities
Distance education
Students
Teachers
1) What teaching tools can I provide?
2) What learning tools can I provide?
Manipal
Tech
Manipal
Medicine
Manipal
Management
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MULN – Business Feature Areas
Content Authoring
Environment
Classroom Recording / transform
Environment
Connected
Content Repository
Extended
Learning Environment
• Author learning material
• Workflows for authoring
and publishing
• Control Content Quality
• Virtual learning communities
from institutes to extended
classroom
•Learning Material, study
plans and monitor learning
• Assessment and evaluation,
grade books
• Individual workspaces and
portfolios
• Digitally Capture and
complete run of a
classroom course and
transform into useful self
learning material
• Create extended rich-
media classrooms
• Across Manipal shared
repository of content
• rich semantic indexes and
common ontology
• tools to add user generated
content and for licensing,
rights management and
ownership
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Challenges of Content Repositories
Communities interact and in that process they create valuable information
Content outlasts everything, even its own creators – both human as well as technology and tools.
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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 group
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.
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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 products (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
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Architecture, Design Challenges
Designing information models that are “application independent”
Design for constitutions and not for “protocols”
Shift from
Integration to Interoperability
Interoperability to Interaction
Embed the “workflow” in the “content” and not the “data” in the “workflow”
Machine processible “meaning”
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Static Equilibrium to Dynamic Harmony
? True
False
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Computing & Society – Evolution of Social Applications
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Role of Technology
capturing everything
storing everything
distributing everything
enabling many-to-many communication
creating value from the data
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•Expert Designed
Directory
•Cross References
(One Url can be at
most at 3 places)
•Storage and linking
are delinked
•Only Tags, content is
not stored
•Community
Organization of
Content
Web 1.0
Web 2.0
Web 2.0/3.0
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Social Web Social + Semantic Web
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Semantics and Ontologies Modeling “meaning” for machines
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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, 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)..
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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” APIs
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?
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Semantics – They Mystery of Meaning
The quest is 5000 years old!
Many approaches, enquiries, investigations and theories
The word for Object in Sanskrit is “padArThaM” – literally “the meaning of the word”
The crux of Ontology:
“astitva, jñānēyatva, abhidēyatva”
“whatever is, is knowable, is namable”
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Basic stance of ontology is –
meanings are entities, events and relations
Meanings occur in Cognition
Central issue of ontological engineering is –
how to specify meaning for robots or computational agents
Meanings are impressed in cognition & are expressed in natural language
impress-meanings recur
Ontology seeks entitative account of such recurrence
Ontological engineering seeks automation of such account
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Formal Vs. Descriptive Ontology
Formal Ontology is Reasoning among entities
Formal Logic is reasoning among Propositions
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Logic of propositions vs. Reasoning among entities
A Structural Specification
Company has Employees
Company has promoters
Company has a management team
Company has a board of directors
Managers are employees
Employees have name, address, role, designation, Salary
Company has temporary staff.
Company has a certain number of business units
Company has a certain operational, support functions
A Semantic Specification
Company is owned by promoters (Power)
Company is controlled by the management team/founders (control)
Employees are the company (existence)
Company is engaged in a certain business operations. (function)
Company needs certain support functions (quality)
Company makes profit (causal)
Company pays taxes
Consultants are associated with the company. (temporal)
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Syntax, Structure and Semantics
Semantics:
Meaning &
Use of Data
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US Library of Congress Top Level Hierarchy:
D: History (general)
DA: Great Britain
DB: Austria
DC: France
DD: Germany
DE: Mediterranean
DF: Greece
DG: Italy
DH: Low Countries
DJ: Netherlands
DK: Former Soviet Union
DL: Scandinavia DP:
Iberian Peninsula DQ:
Switzerland
DR: Balkan Peninsula
DS: Asia
DT: Africa
DU: Oceania
•Designed to Optimize for Space.
•One Entry can only be at one
place
•Who decides the Categories?
• Same Metaphor translated in early
information systems – File Systems,
Hierarchical Databases
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Categories Vs. Tags
Taxonomies and
Folksonomies
•Different functions
•Different ways of
organizing information,
•Different world views ?
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Ontology: What can we make of this?
Meaning in the text Interpretable by common sense
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Ontologies: Data “enriched” with meta-data?
What about relationships between entities and what they mean?
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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..
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
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Ontology: Reality is in Relationships
Meaning is in Relationships between the entities
The entity is described, is known, is understood using its relationships to other entities
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The semantics of computing
Ontology Language/ Representation Spectrum
Is Disjoint Subclass of
with transitivity
property
Modal Logic
Logical Theory
Thesaurus Has Narrower Meaning Than
Taxonomy
Is Sub-Classification of
Conceptual Model Is Subclass of
DB Schemas, XML Schema
First Order Logic
Relational Model
XML
ER
Extended ER
Description Logic DAML+OIL, OWL, UML
RDFS, XTM
Syntactic Interoperability
Structural Interoperability
Semantic Interoperability
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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?
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Semantic Web Application Lifecycle
Build Information Model
Create Assimilation Models
& Aggregate knowledge
Refine/Evolve
Information Model
Semantic Query
Server
Retrieve and Use Semantic Data
Ontology Editors:
Protégé, TopBraid
Composer
Technologies:
GRDDL, RDFizers,
OWLs, Automatic
Annotation
RDF Stores:
Mulgara, Sesame
Programming: Jena
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Semantic Web Application Lifecycle
Information Modelling
Build Ontology (model level representation)
Information Assimilation
Populate Knowledgebase from various sources
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
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Architecture Stack of Semantic Technologies
Application
Semantic Middleware
e.g. Semantic SOA
Semantic Technology Stack
SPARQL Processor
RDF Store
Inference Engine
HTTP SOAP
Programming API
Relational
Store
RDF-SQL
Adaptor
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Semantic Web Technologies
Source: W3C
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The Perceptron.Net Use case
A rich Cultural Informatics environment designed to
Create, Collect, Categorize any type of cultural artifact – Music, Literature, Travel, Leisure, 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…
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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”
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
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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.
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.
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Song/Composition Dimension