Taxonomy and Knowledge Organization Taxonomy in Context

1. Taxonomy and Knowledge OrganizationTaxonomy in Context Tom ReamyChief Knowledge Architect KAPS Group Knowledge Architecture Professional Services http://www.kapsgroup.com

2. Agenda • Introduction: Time for Taxonomies • Business Case for Taxonomies • Taxonomy in the Organization: Intellectual Infrastructure • Content, People, Technology, Activities • Infrastructure Approach to Taxonomy • Staffing and Activities • Taxonomy Development • Conclusion • Future Directions • Building on the Intellectual Infrastructure

3. KAPS Group • Knowledge Architecture Professional Services (KAPS) • Consulting, strategy recommendations • Knowledge architecture audits • Partners – Convera, Inxight, FAST, and others • Taxonomies: Enterprise, Marketing, Insurance, etc. • Taxonomy customization • Intellectual infrastructure for organizations • Knowledge organization, technology, people and processes • Search, content management, portals, collaboration, knowledge management, e-learning, etc.

4. Time for Taxonomies • Taxonomy Time: Technology is not delivering • Professionals spend more time looking for information than using it • 50% of them spend > 2 hours a day looking • Search not enough – text strings vs. concepts • Relevance isn’t very relevant • Data mining misses 80% of significant content • Text mining needs more structure (taxonomies) • 70% of all ECM initiatives will fail due to an underinvestmant in taxonomy – Gartner.

5. Time for Taxonomies: Word of Caution • Taxonomy is not the answer • Is this a taxonomy? • Inventories, catalogs, classifications, categorization schemas, thesauri, controlled vocabularies • Taxonomy not enough – need other structures • Metadata, facets • Taxonomies have to be used to be useful • How to fail: • Taxonomy as a project • Taxonomy as a search engine project afterthought

6. Business Case for Taxonomies:The Right Context • Traditional Metrics • Time Savings – 22 minutes per user per day = $1Mil a Year • Apply to your organization – customer service, content creation, knowledge industry • Cost of not-finding = re-creating content • Research • Advantages of Browsing – Marti Hearst, Chen and Dumais • Nielsen – “Poor classification costs a 10,000 user organization $10M each year – about $1,000 per employee.” • Stories • Pain points, success and failure – in your corporate language

7. Business Case for Taxonomies:IDC White Paper • Information Tasks • Email – 14.5 hours a week • Create documents – 13.3 hours a week • Search – 9.5 hours a week • Gather information for documents – 8.3 hours a week • Find and organize documents – 6.8 hours a week • Gartner: “Business spend an estimated $750 Billion annually seeking information necessary to do their job. 30-40% of a knowledge worker’s time is spent managing documents.”

8. Business Case for Taxonomies:IDC White Paper • Time Wasted • Reformat information - $5.7 million per 1,000 per year (400M) • Not finding information - $5.3 million per 1,000 (370M) • Recreating content - $4.5 Million per 1,000 (315M) • Small Percent Gain = large savings • 1% - $10 million • 5% - $50 million • 10% - $100 million

9. Business Case for Taxonomies:The Right Context • Justification • Search Engine - $500K-$2Mil • Content Management - $500K-$2Mil • Portal - $500-$2Mil • Plus maintenance and employee costs • Taxonomy • Small comparative cost • Needed to get full value from all the above • ROI – asking the wrong question • What is ROI for having an HR department? • What is ROI for organizing your company?

10. Business Case for Taxonomies:The Right Context – Infrastructure Approach • Integrated Enterprise requires both an infrastructure team and distributed expertise. • Software and SME’s is not the answer - keywords • Taxonomies not stand alone • Metadata, controlled vocabularies, synonyms, etc. • Variety of taxonomies, plus categorization, classification, etc. • Important to know the differences, when to use which • Advanced Cognitive Differences • Panda, monkey, banana • Infrastructure as Operating System • Word vs. Word Perfect • Instead of sharing clipboard, share information and knowledge.

11. Infrastructure Model of Taxonomy DevelopmentTaxonomy in Basic 4 Contexts • 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

12. Taxonomy in ContextStructuring 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

13. Taxonomy in Context: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)

14. Taxonomy in Context: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

15. Taxonomy in Context: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

16. Infrastructure Solutions: The start and foundationKnowledge Architecture Audit • Knowledge Map - Understand what you have, what you are, what you want • The foundation of the foundation • Contextual interviews, content analysis, surveys, focus groups, ethnographic studies • Category modeling – “Intertwingledness” -learning new categories influenced by other, related categories • Natural level categories mapped to communities, activities • Novice prefer higher levels • Balance of informative and distinctiveness • Living, breathing, evolving foundation is the goal

17. Infrastructure Solutions: ResourcesPeople 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

18. Infrastructure Solutions: Resources 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 • Design content value structure – more nuanced than good / poor content.

19. Infrastructure Solutions: ResourcesPeople and Processes: Facilitating Knowledge Transfer • Need for Facilitators • Amazon hiring humans to refine recommendations • Google – humans answering queries • Facilitate projects, KM project teams • Facilitate knowledge capture in meetings, best practices • Answering online questions, facilitating online discussions, networking within a community • Design and run KM forums, education and innovation fairs • Work with content experts to develop training, incorporate intelligence into applications • Support innovation, knowledge creation in communities

20. Infrastructure Solutions: ResourcesPeople 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

21. Infrastructure Solutions: ResourcesTechnology • Taxonomy Management • Text and Visualization • Entity and Fact Extraction • Text Mining • Search for professionals • Different needs, different interfaces • Integration Platform technology • Enterprise Content Management

22. Infrastructure Solutions: Taxonomy DevelopmentTaxonomy Model • Enterprise Taxonomy • No single subject matter taxonomy • Need an ontology of facets or domains • Standards and Customization • Balance of corporate communication and departmental specifics • At what level are differences represented? • Customize pre-defined taxonomy – additional structure, add synonyms and acronyms and vocabulary • Enterprise Facet Model: • Actors, Events, Functions, Locations, Objects, Information Resources • Combine and map to subject domains

23. Infrastructure Solutions: Taxonomy DevelopmentInitial Development / Customization • Combination of top down and bottom up (and Essences) • Top: Design an ontology, facet selection • Bottom: Vocabulary extraction – documents, search logs, interview authors and users • Develop essential examples (Prototypes) • Most Intuitive Level – genus (oak, maple, rabbit) • Quintessential Chair – all the essential characteristics, no more • Map the taxonomy to communities and activities • Category differences • Vocabulary differences

24. Infrastructure Solutions: Taxonomy DevelopmentEvaluate and Refine • Formal Evaluation • Quality of corpus – size, homogeneity, representative • Breadth of coverage – main ideas, outlier ideas (see next) • Structure – balance of depth and width • Practical Evaluation • Test in real life application • Test node labels with Subject Matter Experts, representative users and documents • Test with representative key concepts • Test for un-representative strange little concepts that only mean something to a few people but the people and ideas are key and are normally impossible to find

25. Future Directions: Knowledge Organization • New analytic methods • Cognitive anthropology, history of ideas, ESNA • New metadata schemas • SCORM, RDF and semantic Web • Learning and knowledge objects • New people models • Bloom’s Taxonomy, Gardner’s 7 Intelligences • Advanced personalization • Community-based, cognitive-based • Adaptive, dynamic presentation variations

26. Before you view: Agent keeps you up to date Your connections to content and communities, your preferences Your history and the history of other members of your communities When you add/change content Suggests categorization value, metadata values Routes to appropriate content and communities Prompt on unusual connections Pre-existing content Related content Regulatory issues Ask the question – route to experts? When you look for information Taxonomy-based dynamic browse Entities People, companies, wells Related content Regulatory, patents, BI-CI Geological data News stories Dictionaries, USGS data, databases Experts Ask questions, chat When you use information Communities Search, chat, email Performance aids, classes Stories The Contextual Desktop: Document, List of Documents, Applications Screen

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