essentials of knowledge architecture tom reamy chief knowledge architect kaps group knowledge...
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Essentials of Knowledge Architecture
Tom ReamyChief Knowledge Architect
KAPS Group
Knowledge Architecture Professional Services
http://www.kapsgroup.com
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Agenda
Introduction – Crisis in KM
Essentials of Knowledge Architecture
Knowledge Structures
Conclusion
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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
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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
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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
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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)– E-learning and KM fusion – why not?
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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
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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
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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
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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)
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Knowledge Structures
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– 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
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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
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Knowledge Structures: Ontology
Music Instruments
ViolinsBluegrass
Violinists
Musicians
uses
uses
is ais a
create
is a
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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)
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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– 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
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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
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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
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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
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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
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