1. Essentials of Knowledge Architecture Tom Reamy Chief Knowledge Architect KAPS Group Knowledge Architecture Professional Services http://www.kapsgroup.com

2. 2 Agenda  Introduction – Crisis in KM  Essentials of Knowledge Architecture  Knowledge Structures  Conclusion

3. 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. 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. 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. 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. 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. 8 Knowledge Architecture Basic 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 21 Two Types of Taxonomies: Formal

22. 22 Two Types of Taxonomies: Browse and Formal Browse Taxonomy – Yahoo

23. 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. 24 Knowledge Structures Semantic 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. 25 Knowledge Structures: Ontology  Music Instruments Violins Bluegrass Violinists Musicians uses is a create is a

26. 26 Knowledge Structures Topic Maps  ISO Standard  See www.topicmaps.orgwww.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. 27 Knowledge Structure: Topic Maps 

28. 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. 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. 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. 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. 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

33. Questions? Tom Reamy tomr@kapsgroup.com KAPS Group Knowledge Architecture Professional Services http://www.kapsgroup.com