Essentials of Knowledge Architecture

Tom ReamyChief Knowledge Architect

KAPS Group

Knowledge Architecture Professional Services

http://www.kapsgroup.com

2

Agenda

Introduction – Crisis in KM

Essentials of Knowledge Architecture

Knowledge Structures

Conclusion

3

Crisis in KM

Death of KM? David Snowden and others CIO reporting to CFO, not CEO Second or Third Identity Crisis – lurch not build Web 2.0 is not the answer

– At some point we have to stop networking and start working Boutique (little km)

– Peripheral to main activities of the organization– KM as collaboration (COP’s, expertise location), Best Practices– KM as high end strategy – management fad– Divorced from Information

4

History of Ideas – Knowledge & Culture in KM

Only two ideas – Tacit Knowledge, DIKW model– Used to avoid discussions of nature of knowledge– Tacit – no such thing as pure tacit– Isolates knowledge from information – continuum– Restricts meaning of knowledge – leaves out body of knowledge

KM and Culture– Too often – culture = readiness for KM Programs– Need anthropology culture – IT, HR, Sales as tribes

5

Essential Features of big KM

Semantic Infrastructure / Foundation of Theory – big vision, small integrated (cheaper and better) projects

– Dynamic map of content, communities (formal and informal), business and information activities and behaviors, technologies

– An infrastructure team and distributed expertise (Web 2.0 and 3.0) Better Models of Knowledge / visualizations

– Body of K - taxonomies, facets, books, stories, ontology, K map– Personal knowledge – cognitive science, linguistics

Importance of language and categorization KM built on foundation of knowledge architecture

6

What is Knowledge Architecture?

Knowledge Architecture is an interdisciplinary field that is concerned with designing, creating, applying, and refining an infrastructure for the flow of knowledge throughout an organization.

Knowledge Architecture is information architecture + library science + cognitive science

Essential Partner – Education (Knowledge transfer)– E-learning and KM fusion – why not?

7

Why Knowledge Architecture?

Foundation for Essential Knowledge Management Immanuel Kant

– Concepts without percepts are empty– Percepts without concepts are blind

Knowledge Management– KM without applications is empty (Strategy Only)– Applications without KA are blind (IT based KM)

Interpentration of Opposites Cognitive Difference – Geography of Thought

• Panda, monkey, banana

8

Knowledge ArchitectureBasic 4 Contexts of Structure Ideas – Content Structure

– Language and Mind of your organization– Applications - exchange meaning, not data

People – Company Structure– Communities, Users, Central Team

Activities – Business processes and procedures– Central team - establish standards, facilitate

Technology / Things– CMS, Search, portals, taxonomy tools– Applications – BI, CI, Text Mining

9

Knowledge Architecture Structuring Content All kinds of content and Content Structures

– Structured and unstructured, Internet and desktop

Metadata standards – Dublin core+– Keywords - poor performance – Need controlled vocabulary, taxonomies, semantic network

Other Metadata – Document Type

• Form, policy, how-to, etc.– Audience

• Role, function, expertise, information behaviors– Best bets metadata

Facets – entities and ideas– Wine.com

10

Knowledge Architecture :Structuring People

Individual People– Tacit knowledge, information behaviors– Advanced personalization – category priority

• Sales – forms ---- New Account Form

• Accountant ---- New Accounts ---- Forms

Communities– Variety of types – map of formal and informal– Variety of subject matter – vaccines, research, scuba– Variety of communication channels and information behaviors– Community-specific vocabularies, need for inter-community

communication (Cortical organization model)

11

Knowledge Architecture :Structuring Processes and Technology Technology: infrastructure and applications

– Enterprise platforms: from creation to retrieval to application– Taxonomy as the computer network

• Applications – integrated meaning, not just data

Creation – content management, innovation, communities of practice (CoPs)

– When, who, how, and how much structure to add– Workflow with meaning, distributed subject matter experts (SMEs)

and centralized teams

Retrieval – standalone and embedded in applications and business processes

– Portals, collaboration, text mining, business intelligence, CRM

12

Knowledge Architecture : The Integrating Infrastructure Starting point: knowledge architecture audit, K-Map

– Social network analysis, information behaviors People – knowledge architecture team

– Infrastructure activities – taxonomies, analytics, best bets– Facilitation – knowledge transfer, partner with SMEs

“Taxonomies” of content, people, and activities– Dynamic Dimension – complexity not chaos– Analytics based on concepts, information behaviors

Taxonomy as part of a foundation, not a project– In an Infrastructure Context

13

Knowledge Architecture People and Processes: Roles and Functions Knowledge Architect and learning object designers Knowledge engineers and cognitive anthropologists Knowledge facilitators and trainers and librarians Part Time

– Librarians and information architects– Corporate communication editors and writers

Partners– IT, web developers, applications programmers– Business analysts and project managers

14

Knowledge Architecture Skills: Backgrounds Interdisciplinary, Generalists, Idea and People people Library Science, Information Architecture Anthropology, Cognitive Science Learning, Education, History of Ideas Artificial Intelligence, Linguistics Business Intelligence, Database Administration

15

Knowledge Architecture People and Processes: Central Team

Central Team supported by software and offering services– Creating, acquiring, evaluating taxonomies, metadata standards,

vocabularies– Input into technology decisions and design – content management,

portals, search– Socializing the benefits of metadata, creating a content culture– Evaluating metadata quality, facilitating author metadata– Analyzing the results of using metadata, how communities are using– Research metadata theory, user centric metadata – Create framework for 2.0 – blogs, wiki’s

16

Knowledge Architecture People and Processes: Location of Team

KM/KA Dept. – Cross Organizational, Interdisciplinary Balance of dedicated and virtual, partners

– Library, Training, IT, HR, Corporate Communication

Balance of central and distributed Industry variation

– Pharmaceutical – dedicated department, major place in the organization

– Insurance – Small central group with partners– Beans – a librarian and part time functions

Which design – knowledge architecture audit

17

Knowledge Architecture Technology

Taxonomy Management – Text and Visualization

Entity and Fact Extraction Text Mining Search for professionals

– Different needs, different interfaces

Integration Platform technology– Enterprise Content Management

18

Knowledge Architecture Services Knowledge Transfer – need for facilitators

– even Amazon is moving away from automated recommendations

Facilitate projects, KM Project teams – Core group of consultants and K managers

Facilitate knowledge capture in meetings Answering online questions, facilitating online discussions,

networking within a community Design and run forums, education fairs, etc. Curriculum developers work with content experts, identify training

requirements, design learning objectives, develop courses

19

Knowledge Architecture Services Infrastructure Activities

– Integrate taxonomy across the company• Content, communities, activities• Link documents that relate to safety with the training curriculum.

– Design content repositories, update and adapt categorization – Package knowledge into K objects, combine with stories,

learning histories– Metrics and Measurement – analyze and enhance– Knowledge Architecture Audit

• Enterprise wide • Project scale

20

Knowledge Structures

List of Keywords (Folksonomies) Controlled Vocabularies, Glossaries Thesaurus Browse & Formal Taxonomies Faceted Classifications Semantic Networks / Ontologies Topic Maps Knowledge Maps Stories

21

Two Types of Taxonomies: Formal

22

Two Types of Taxonomies: Browse and Formal

Browse Taxonomy – Yahoo

23

Facets and Dynamic Classification

Facets are not categories– Entities or concepts belong to a category– Entities have facets

Facets are metadata - properties or attributes– Entities or concepts fit into one category– All entities have all facets – defined by set of values

Facets are orthogonal – mutually exclusive – dimensions– An event is not a person is not a document is not a place.

Facets – variety – of units, of structure– Date or price – numerical range– Location – big to small (partonomy)– Winery – alphabetical– Hierarchical - taxonomic

24

Knowledge StructuresSemantic Networks / Ontologies

Ontology more formal XML standards – OWL, DAML Semantic Web – machine understanding RDF – Noun – Verb – Object

– Vice President is Officer Build implications – from properties of Officer Semantic Network – less formal

– Represent large ontologies– Synonyms and variety of relationships

25

Knowledge Structures: Ontology

Music Instruments

ViolinsBluegrass

Violinists

Musicians

uses

uses

is ais a

create

is a

26

Knowledge StructuresTopic Maps

ISO Standard See www.topicmaps.org Topic Maps represent subjects (topics) and associations

and occurrences Similar to semantic networks Ontology defines the types of subjects and types of

relationships Combination of semantic network and other formal

structures (taxonomy or ontology)

27

Knowledge Structure: Topic Maps

28

Knowledge Structure: Knowledge Maps

Knowledge Map - Understand what you have, what you are, what you want

Modularity of Mind – technical, natural, social, language– Gardner – 7 intelligences

Frameworks – Ways of thinking – IT and Humanities:– Correct answer – Depth of Knowledge– Egalitarian – Hierarchy & Status– Multiple snippets – reading books– Projects – Infrastructure– Revolution vs. Evolution

Impact of K models and support for multiple models

29

Knowledge Structures: Which one to use?

Level 1 – keywords, glossaries, acronym lists, search logs– Resources, inputs into upper levels

Level 2 – Thesaurus, Taxonomies– Semantic Resource – foundation for applications, metadata

Level 3 – Facets, Ontologies, semantic networks, topic maps, Stories

– Applications

Level 4 – Knowledge maps – Strategic Resource

30

Category Theory

Hierarchical Nature of Categories– Computed or Pre-stored

Typicality / Prototype– Robin vs. Ostrich Category modeling – “Intertwingledness” -learning new

categories influenced by other, related categories Basic Level Categories

– Mammal – Dog – Golden Retriever– Balance of Distinctiveness and # of Properties (informativeness)– Level of Expertise = One higher or lower

Implications – taxonomy type, depth, folksonomy

31

Conclusion

Knowledge Architecture is a new foundation for KM KA is an infrastructure solution, not a project KA brings knowledge and knowledge structures back to KM Variety of information and knowledge structures

– Important to know what will solve what

Taxonomies and Facets are foundation elements A strong theoretical foundation is important and practical Web 2.0/Folksonomies are not the answer

32

Resources

Books– Women, Fire, and Dangerous Things

• George Lakoff– Knowledge, Concepts, and Categories

• Koen Lamberts and David Shanks– The Stuff of Thought – Steven Pinker– The Mind and Its Stories – Patrick Colm Hogan– The Literary Animal – ed. Jonathan Gottschall and David

Sloan Wilson Articles

– The Power of Stories – Scientific American Mind – August/September 2008

Questions?

Tom Reamytomr@kapsgroup.com

KAPS Group

Knowledge Architecture Professional Services

http://www.kapsgroup.com