Taxonomy and Knowledge Organization Taxonomy in Context Tom Reamy Chief Knowledge Architect KAPS Group Knowledge Architecture Professional Services

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 – – 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 % 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 Development Taxonomy 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 Context 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

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 foundation Knowledge 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: Resources 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

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: Resources People 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: Resources 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

21 Infrastructure Solutions: Resources 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

22 Infrastructure Solutions: Taxonomy Development Taxonomy 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 Development Initial 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 Development Evaluate 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 The Contextual Desktop: Document, List of Documents, Applications Screen  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, – Performance aids, classes – Stories

Questions? Tom Reamy KAPS Group Knowledge Architecture Professional Services