Essentials of Knowledge Architecture

1. 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 FormalBrowse 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 Instruments Music is a is a create Bluegrass Violins uses Musicians uses is a Violinists

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

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