web3.0 seminar wipro-session1-pursuitofmeaning

24-06-2010

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Session I – The Pursuit and Power of

Meaning

Nagaraju Pappu

June 2010

Web 3.0, Semantics &

Enterprise Computing

www.canopusconsulting.com

© Canopus Consulting

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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

web3.0 seminar wipro-session1-pursuitofmeaning

Technology

true false canopus consulting

user community

extension community

entire community

community network

useful content

blogs web

social computing