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Taxonomy Development An Infrastructure Model Tom Reamy Chief Knowledge Architect KAPS Group Knowledge Architecture Professional Services

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Presentation on theme: "Taxonomy Development An Infrastructure Model Tom Reamy Chief Knowledge Architect KAPS Group Knowledge Architecture Professional Services"— Presentation transcript:

1 Taxonomy Development An Infrastructure Model Tom Reamy Chief Knowledge Architect KAPS Group Knowledge Architecture Professional Services

2 2 Agenda Introduction Type of Taxonomies The Enterprise Context – Making the Business Case Infrastructure Model of Taxonomy Development – Taxonomy in 4 Contexts Content, People, Processes, Technology Infrastructure Solutions – the Elements Applying the Model – Practical Dimension – Starting and Resources Conclusion

3 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 4 Two Types of Taxonomies: Browse and Formal Browse Taxonomy – Yahoo

5 5 Two Types of Taxonomies: Formal

6 6 Browse Taxonomies: Strengths and Weaknesses Strengths: Browse is better than search – Context and discovery – Browse by task, type, etc. Weaknesses: – Mix of organization Catalogs, alphabetical listings, inventories Subject matter, functional, publisher, document type – Vocabulary and nomenclature Issues – Problems with maintenance, new material – Poor granularity and little relationship between parts. Web site unit of organization – No foundation for standards

7 7 Formal Taxonomies: Strengths and Weaknesses Strengths: – Fixed Resource – little or no maintenance – Communication Platform – share ideas, standards – Infrastructure Resource Controlled vocabulary and keywords More depth, finer granularity Weaknesses: – Difficult to develop and customize – Dont reflect users perspectives Users have to adapt to language

8 8 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

9 9 Faceted Navigation: Strengths and Weaknesses Strengths: – More intuitive – easy to guess what is behind each door 20 questions – we know and use – Dynamic selection of categories Allow multiple perspectives – Trick Users into using Advanced Search wine where color = red, price = x-y, etc.. Weaknesses: – Difficulty of expressing complex relationships Simplicity of internal organization – Loss of Browse Context Difficult to grasp scope and relationships – Limited Domain Applicability – type and size Entities not concepts, documents, web sites

10 10 Dynamic Classification / Faceted navigation Search and browse better than either alone – Categorized search – context – Browse as an advanced search Dynamic search and browse is best – Cant predict all the ways people think Advanced cognitive differences Panda, Monkey, Banana – Cant predict all the questions and activities Intersections of what users are looking for and what documents are often about China and Biotech Economics and Regulatory

11 11 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

12 12 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 workers time is spent managing documents.

13 13 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

14 14 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?

15 15 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

16 16 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 –

17 17 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)

18 18 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

19 19 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

20 20 Taxonomy in Context: The Integrating Infrastructure Integrated Enterprise requires both an infrastructure team and distributed expertise. – Software and SMEs 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 Multiple Applications – Search, browse, content management, portals, BI & CI, etc. Infrastructure as Operating System – Word vs. Word Perfect – Instead of sharing clipboard, share information and knowledge.

21 21 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

22 22 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

23 23 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.

24 24 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

25 25 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

26 26 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

27 27 Taxonomy Development: Tips and Techniques Stage One – How to Begin Step One: Strategic Questions – why, what value from the taxonomy, how are you going to use it – Variety of taxonomies – important to know the differences, when to use what. Step Two: Get a good taxonomist! (or learn) – Library Science+ Cognitive Science + Cognitive Anthropology Step Three: Software Shopping – Automatic Software – Fun Diversion for a rainy day Uneven hierarchy, strange node names, weird clusters – Taxonomy Management, Entity Extraction, Visualization Step Four: Get a good taxonomy! – Glossary, Index, Pull from multiple sources – Get a good document collection

28 28 Infrastructure Solutions: Taxonomy Development Stage Two: 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

29 29 Taxonomy Development: Tips and Techniques Stage Three: Development and/or 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 – Work toward the prototype and out and up and down – Repeat until dizzy or done Map the taxonomy to communities and activities – Category differences – Vocabulary differences

30 30 Taxonomy Development: Tips and Techniques Stage Four: 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 – Kill the verbs – Evaluate speciation steps – understandable and systematic Person – Unwelcome person – Unpleasant person - Selfish person – Avoid binary levels, duplication of contrasts – Primary and secondary education, public and private

31 31 Taxonomy Development: Tips and Techniques Stage Four: Evaluate and Refine Practical Evaluation – Test in real life application – Select representative users and documents – Test node labels with Subject Matter Experts Balance of making sense and jargon – 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

32 32 Sources Books – Women, Fire, and Dangerous Things What Categories Reveal about the Mind George Lakoff – The Geography of Thought Richard E. Nisbett Software – Convera Retrievalware – Inxight Smart Discovery – entity and fact extraction Courses – Convera Taxonomy Certification

33 33 Conclusion Taxonomy development is not just a project – It has no beginning and no end Taxonomy development is not an end in itself – It enables the accomplishment of many ends Taxonomy development is not just about search or browse – It is about language, cognition, and applied intelligence Strategic Vision (articulated by K Map) is important – Even for your under the radar vocabulary project Paying attention to theory is practical – So is adapting your language to business speak

34 34 Conclusion Taxonomies are part of your intellectual infrastructure – Roads, transportation systems not cars or types of cars Taxonomies are part of creating smart organizations – Self aware, capable of learning and evolving Think Big, Start Small, Scale Fast If we really are in a knowledge economy We need to pay attention to – Knowledge!

35 Questions? Tom Reamy KAPS Group Knowledge Architecture Professional Services

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