Knowledge Management

Session 8: Introduction to Information Architecture

Anveshika Shrivastava11th December , 2011

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Symbiosis Centre for Information Technology

KM

Session Agenda

• Introduction to Information Architecture Taxonomy Development Metadata Definition/ frameworks Content Publishing/Localization Search

• Relevance in KM Ecology

• Last GA : KM-GA-5 Presentations

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Introductory session - due to complexity , vastness and rapid evolution of this field

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Recap?

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Recap from last session

• Class Test

• Open Session

• Trends of Knowledge Management

• New Trends Enterprise 3.0 Advanced Visualization Metaverses Crowd Sourcing

• Individual Discussion

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KM

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What is information architecture (IA)?

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Introducing Information Architecture

• Architecting content

• Designing the content pillar of KM

• Business centric organization of content , conforming to universal and organization metadata standards.

• The process or the science or the organizational projects which aims at creating a content structure in order to achieve varied organizational objectives, along with content consolidation and standardization.

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KM

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Why is it needed?

KMWhy is it needed?

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Need for Information Architecture

Humungous content volumes•Varied users and requirements Varied compliance and

organizational requirements

Content Duplication

Not conforming with industry / global

metadata standards

Irregular and Ad-hoc content management –

reactive.

No proper content structure taxonomy

Growing monster – ambiguity about content in silos

Security concerns: Role, Standards

Not optimized Search operations

KMProcesses/Components of Information Architecture

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Major IA Components/

Processes

Taxonomy Development

Metadata Definition/ frameworks

Content Publishing/Localization

Search• Controlled Vocabulary• Synonym Rings

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Why is a taxonomy?

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Taxonomy• A group of things organized and related according to a set of principles

for a specific purpose. - Vivian Bliss

• Categorization and classification scheme of organizational content aligned with business objectives, processes and the end users.

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Cricketer (All-rounder)

Batting Statistics

Runs scoredBatting Average

Bowling Statistics

Wickets takenEconomy

General Statistics

Matches Played Catches etc

L0

L1.0 L2.0

L1.1 L1.2 L2.1 L2.2

L3.1 L3.2

L3.0

Facet of the taxonomy

• Stores organization content such as Document, Project, Content, Records and Rich Media.

• Promotes content storage and reuse• Improves search & navigation

Levels

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Important Attributes of taxonomy

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The Perfect Taxonomy

Alignment with business/

organizational objectives

Alignment with processes

Alignment with end users

For e.g. Compliance needs

For e.g. Optimized Search Crawls – User specific search – compliant with User driven tag based taxonomy

Should make sense to end users

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Types of taxonomy

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The Perfect Taxonomy

Hierarchical

Content Driven

User Driven

System Aggregated

Examples?

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Hierarchical Taxonomy

• Categorization based on domain/functions or “top down” /Topical Browsing

• Compliance / vertical specific categorization rules and descriptions

• Example: ‘Explore view on your laptop for any directory’ , WAND Taxonomies

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Cricketer (All-rounder)

Batting Statistics

Runs scored

Bowling Statistics

Wickets taken

General Statistics

Matches Played

• Unidirectional browsing • Not compatible with BPM systems • Perception centric • Vertical specialization required • Content can be categorized into

multiple folder

Risks

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Content Driven Taxonomy• Content-driven or “bottom up”

• ECM /Flat file structure

• Relies on ECM eForms to store content in DB tables

• Aids retention management

• Retrieval: Search strings , Advanced Searches,

SQL/PERL Queries

• Focus on Content properties

• Example: DB2 SQL Table

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• Too many elements• Users skip it• Drop-down menus are not in synch

across systems

Risks

Select Process Type :

Register Request Register Request

ATM Card Issue ATM Card Issue

Enter Customer:

Mr. XYZ Mr. XYZ

Enter Telephone Number

1234512345

Process Customer Telephone Date

ATM CI XYZ 12345 Mm/dd/yy

ATM CI ZXI 2345 Mm/dd/yy

ATM CI ASW 23456 Mm/dd/yy

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User Driven Taxonomy• Tagging based categorization

• Social networking/Collaboration based content organization

• Content retrieval dependent on search queries/tags

• Not regulated

• Simplistic and flexible

• Example: FlickrTM

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• Very little standardization /quality adherence

• Varied definitions based on personal perception

• Lack of structure/relationships

Risks

Tags: Scenery, Bavaria, Jungle

Like

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System Aggregated Taxonomy• System driven searches, refinement and

categorization

• Content aggregated from feeds (RSS etc), based on keywords, tags

• Cluster technology Automatic categorization

• User can set cluster parameters

• Example: YippyTM, Google NewsTM

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• Not accurate and user friendly at all times. • Dependency on system

learning/intelligence • Dependency on quality of data crawled • Incorrect categorization at times

Risks

Yippy Search Results for SCIT

Combos of taxonomies yield results, say User + Subject etc

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Metadata

• "data about data“ or ‘Metacontent’ or ‘content describing a knowledge asset’

• ‘Properties’ of content

• Dewey Decimal Classification ? (Library)

• Data providing information about one or more aspects of the data

Source: MPEG-21 initiative,Organisation Internationale De Normalisation, Shanghai, October 2002

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Metadata Framework

• A metadata framework is a universal and/or organization specific standardized structure describing organization and development of metadata

• Universal metadata frameworks and initiatives aim at standardizations and extensible use of multimedia resources across a wide range of networks and devices..

• Objective is to provide interoperability for metadata vocabularies and associated content and to promote development and adoption of semantic web.

• Data dictionary: Metadata framework documentation. Standardized and reusable resource descriptions/tags , categorized by namespaces.

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<meta name = “dc: Author” content=“Mr. Old Igor”>

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Metadata Framework :DCMI

• Created by Dublin Core Metadata Initiative (DCMI) , independent entity, Open forum for the development of interoperable online metadata

standards for a broad range of purposes and of business models.

• Dublin Core Metadata Initiative (DCMI) is the industry standard for all metadata frameworks. 15 simple core elements (Dublin Core Metadata Element Set) Additional element refinements. Element Refinements : to define narrower or more specific definition . For

e.g. A dateCreated versus a dateModified

• The terms can be used to describe a full range of web resources: video, images, web pages etc and physical resources such as books and objects like artworks.

• The full set of Dublin Core metadata terms: Metadata Initiative (DCMI) website.

20Source: Wiki

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Dublin Core Metadata Element Set

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DCMI Core Metadata Element

set

Title Creator

Subject

Description

Publisher

Contributor

Date

Type Format

Identifier

Source

Language

Relation

Coverage

Rights

KMMetadata Framework :DCMI Framework Example

22Source: 'The NASA Taxonomy, What Is It and How Do We Use It? 'Jayne Dutra, NASA Office of the CIO, 2004

KMMetadata Framework :DCMI Application Example

23Source: 'The NASA Taxonomy, What Is It and How Do We Use It? 'Jayne Dutra, NASA Office of the CIO, 2004

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Other Metadata Frameworks

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Framework Domain/Utility

vCard File format standard for electronic business cards. For e.g.: name and address information, phone number, URLs, logos, photographs. Example - vCard.BDAY

PRISM Publishing Requirements for Industry Standard Metadata (PRISM) specification defines an XML metadata vocabulary for magazine, news, catalog, book, and mainstream journal content.

RIXML Categorizes, tagging global investment research

And other organization specific frameworks…

Difference between taxonomy & metadata?

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Content Publishing/Localization

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• Process for automated/semi automated , content translation, in a multi language set up.

• Powered by NLP

• E.g. Google TranslateTM

• Movement from Multi Language DB table design to on the fly translation

Content Localization

• Process for managing Site/Microsite /Microcommunication / Microblogs communication/publishing

• Content Publishing on Demand

Content Publishing

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Search

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• Crawling for electronic content based on search query

• Many Products such as FASTTM, VivismoTM etc

• Many types: Boolean , Natural Language, Field Specific

• Aided by IA elements such as Controlled Vocabulary , Synonym Rings

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Search: Controlled Vocabulary & Rings

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• Metadata value allocation through a controlled list of content Aids search Consistent resource descriptions and DB coupling

• For e.g. Select State ‘Maharashtra’ <drop down>

Controlled Vocabulary

• Synonym Ring: Alternative label of a content or a synonym , also encapsulates spelling mistakes

• Homonym Rings: Share the same spelling and the same pronunciation but have different meanings. Also includes Folksonomies for Web-specific collaborative, perceptual classification. Folksonomies : Tags

• Central search reference enables accurate search results

• Synonym Communication: Mail, E Mail, Letter

• Folksnomy Kingfisher : Bird, Beer Brand, Airlines etc

Synonym/Homonym Rings

KMRelevance in KM Ecology

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Element: publisher

Definition: Organizational entity that publishes the content

Generation: Controlled vocabulary

Values: ‘Bu 1’‘Bu 2’‘Bu 3’‘Bu 4’

Requirement: Optional

• IA practices and processes are vital for operationalizing the KM strategy

• Acts as a ‘content parser’ , aggregating and refining generated knowledge.

• Promotes content standardizations and knowledge interoperability

• Vital for building an organizational semantic web whicvh conforms to global standards.

• Grassroots /genetic Removal of silos !

Example of KM ~ IA coupling

KMRecap – Last Group Activity # 5 (KM-GA-5) for 11/12/2011: KM AS-IS DICE for BTB

• For the KM Dracula Castle Case Study , map the AS-IS knowledge ecology

• Identify existing Knowledge Populations within the BTB’s knowledge community

• Describe various KM DICE Components and interactions: K-Distribution, K-Interaction, K-Competition and K-Evolution , in AS-IS context.

• Identify some 'Double-loop' learning issues, in AS-IS context.

• Presentation: 11h December 2011 (Session 8)

• Slide Limit: 5 (Excluding Title and Thank you slides)

• Each Group’s time limit: 15 minutes

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Marks for KM-GA-6 assimilated into final presentation

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End of KM –S8

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Two more to go..