Transcript Taxonomy Development Workshop

Essentials of
Knowledge Architecture
Tom Reamy
Chief Knowledge Architect
KAPS Group
Knowledge Architecture Professional Services
http://www.kapsgroup.com
Agenda
 Introduction – Crisis in KM
 Essentials of Knowledge Architecture
 Knowledge Structures
 Conclusion
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Crisis in KM
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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
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At some point we have to stop networking and start working
 Boutique (little km)
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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
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History of Ideas – Knowledge & Culture in KM
 Only two ideas – Tacit Knowledge, DIKW model
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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
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Essential Features of big KM
 Semantic Infrastructure / Foundation of Theory – big vision, small
integrated (cheaper and better) projects
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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
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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
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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)
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E-learning and KM fusion – why not?
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Why Knowledge Architecture?
 Foundation for Essential Knowledge Management
 Immanuel Kant
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Concepts without percepts are empty
Percepts without concepts are blind
 Knowledge Management
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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
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Knowledge Architecture
Basic 4 Contexts of Structure
 Ideas – Content Structure
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Language and Mind of your organization
Applications - exchange meaning, not data
 People – Company Structure
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Communities, Users, Central Team
 Activities – Business processes and procedures
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Central team - establish standards, facilitate
 Technology / Things
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CMS, Search, portals, taxonomy tools
Applications – BI, CI, Text Mining
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Knowledge Architecture
Structuring Content
 All kinds of content and Content Structures
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Structured and unstructured, Internet and desktop
 Metadata standards – Dublin core+
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Keywords - poor performance
– Need controlled vocabulary, taxonomies, semantic network
 Other Metadata
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Document Type
• Form, policy, how-to, etc.
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Audience
• Role, function, expertise, information behaviors
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Best bets metadata
 Facets – entities and ideas
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Wine.com
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Knowledge Architecture :
Structuring People
 Individual People
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Tacit knowledge, information behaviors
Advanced personalization – category priority
• Sales – forms ---- New Account Form
• Accountant ---- New Accounts ---- Forms
 Communities
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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)
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Knowledge Architecture :
Structuring Processes and Technology
 Technology: infrastructure and applications
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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)
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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
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Portals, collaboration, text mining, business intelligence, CRM
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Knowledge Architecture :
The Integrating Infrastructure
 Starting point: knowledge architecture audit, K-Map
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Social network analysis, information behaviors
 People – knowledge architecture team
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Infrastructure activities – taxonomies, analytics, best bets
Facilitation – knowledge transfer, partner with SMEs
 “Taxonomies” of content, people, and activities
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Dynamic Dimension – complexity not chaos
Analytics based on concepts, information behaviors
 Taxonomy as part of a foundation, not a project
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In an Infrastructure Context
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Knowledge Architecture
People and Processes: Roles and Functions
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Knowledge Architect and learning object designers
Knowledge engineers and cognitive anthropologists
Knowledge facilitators and trainers and librarians
Part Time
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Librarians and information architects
Corporate communication editors and writers
 Partners
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IT, web developers, applications programmers
Business analysts and project managers
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Knowledge Architecture
Skills: Backgrounds
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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
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Knowledge Architecture
People and Processes: Central Team
 Central Team supported by software and offering services
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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
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Knowledge Architecture
People and Processes: Location of Team
 KM/KA Dept. – Cross Organizational, Interdisciplinary
 Balance of dedicated and virtual, partners
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Library, Training, IT, HR, Corporate Communication
 Balance of central and distributed
 Industry variation
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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
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Knowledge Architecture
Technology
 Taxonomy Management
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Text and Visualization
 Entity and Fact Extraction
 Text Mining
 Search for professionals
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Different needs, different interfaces
 Integration Platform technology
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Enterprise Content Management
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Knowledge Architecture
Services
 Knowledge Transfer – need for facilitators
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even Amazon is moving away from automated recommendations
 Facilitate projects, KM Project teams
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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
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Knowledge Architecture
Services
 Infrastructure Activities
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Integrate taxonomy across the company
• Content, communities, activities
• Link documents that relate to safety with the training curriculum.
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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
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Knowledge Structures
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List of Keywords (Folksonomies)
Controlled Vocabularies, Glossaries
Thesaurus
Browse & Formal Taxonomies
Faceted Classifications
Semantic Networks / Ontologies
Topic Maps
Knowledge Maps
Stories
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Two Types of Taxonomies: Formal
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Two Types of Taxonomies: Browse and Formal
Browse Taxonomy – Yahoo
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Facets and Dynamic Classification
 Facets are not categories
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Entities or concepts belong to a category
Entities have facets
 Facets are metadata - properties or attributes
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Entities or concepts fit into one category
All entities have all facets – defined by set of values
 Facets are orthogonal – mutually exclusive – dimensions
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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
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Knowledge Structures
Semantic Networks / Ontologies
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Ontology more formal
XML standards – OWL, DAML
Semantic Web – machine understanding
RDF – Noun – Verb – Object
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Vice President is Officer
 Build implications – from properties of Officer
 Semantic Network – less formal
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Represent large ontologies
Synonyms and variety of relationships
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Knowledge Structures: Ontology
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Instruments
Music
is a
is a
create
Bluegrass
uses
Violins
uses
Musicians
is a
Violinists
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Knowledge Structures
Topic 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)
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Knowledge Structure: Topic Maps
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Knowledge Structure: Knowledge Maps
 Knowledge Map - Understand what you have, what you
are, what you want
 Modularity of Mind – technical, natural, social, language
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Gardner – 7 intelligences
 Frameworks – Ways of thinking – IT and Humanities:
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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
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Knowledge Structures: Which one to use?
 Level 1 – keywords, glossaries, acronym lists, search logs
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Resources, inputs into upper levels
 Level 2 – Thesaurus, Taxonomies
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Semantic Resource – foundation for applications, metadata
 Level 3 – Facets, Ontologies, semantic networks, topic
maps, Stories
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Applications
 Level 4 – Knowledge maps
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Strategic Resource
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Category Theory
 Hierarchical Nature of Categories
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Computed or Pre-stored
 Typicality / Prototype– Robin vs. Ostrich
 Category modeling – “Intertwingledness” -learning new
categories influenced by other, related categories
 Basic Level Categories
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Mammal – Dog – Golden Retriever
Balance of Distinctiveness and # of Properties
(informativeness)
Level of Expertise = One higher or lower
 Implications – taxonomy type, depth, folksonomy
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Conclusion
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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
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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
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Resources
 Books
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Women, Fire, and Dangerous Things
• George Lakoff
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Knowledge, Concepts, and Categories
• Koen Lamberts and David Shanks
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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
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The Power of Stories – Scientific American Mind –
August/September 2008
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Questions?
Tom Reamy
[email protected]
KAPS Group
Knowledge Architecture Professional Services
http://www.kapsgroup.com