Unstructured Content Management Taxonomic Publishing Models

1. Unstructured Content Management Taxonomic Publishing Models Tom ReamyChief Knowledge Architect KAPS Group Knowledge Architecture Professional Services http://www.kapsgroup.com tomr@kapsgroup.com

2. Agenda • KAPS Group and Knowledge Architecture • Current State of Content Management • Taxonomies and Content Management • What, Why, and How • Taxonomic Content Management • Infrastructure Content Management • Technology, Teams, Taxonomies • Beyond Taxonomies • Knowledge Objects, Semantic Web, Personas, etc.

3. KAPS Group • KAPS Background • Knowledge Architecture Consultants • Intellectual Infrastructure: Content, Tools, People • E-Learning and Information Architecture • Knowledge Architecture Audit • Social Network Analysis and Business Process • Professional Services partner to Search, Content Management & Categorization Companies

4. Current State of Content Management • Forrester Research: • Current content management systems are “immature”. • High Cost, Proprietary technology • Poor implementation, Difficult to maintain & customize • CM is good at: • Software, system integration • Version control, work flow • Decoupling content and presentation • Part of a Broader problem • Delphi Survey • 68% finding information is difficult • 50% spend more than 2 hours a day looking

5. Current State of Content Management • Content Management • Strong on management, weak on content • Content is a black box – simply moved around • What is missing is the meaning dimension • In-depth and articulated understanding of content • Perceived Solution – Delphi Survey – Taxonomy • 90% plan on taxonomy strategy in 24 months • 76% taxonomy is important • Taxonomy: necessary but not sufficient

6. Content Management and Taxonomy: What? • Formal Taxonomies • Linnaeus – Taxonomy of life • Only relationship is “Is Kind Of” • Browse Taxonomies (Informal) • Yahoo – Hierarchical • Variety of relationships • Classifications and Categorization • Metaphorical Taxonomies • Thesaurus, catalog, index, site map

7. Content Management and Taxonomy: What? • What makes a good taxonomy? • Formal: Quality Metrics • Corpus, Coverage, Nomenclature, terminology, dependency • Mixed classes, verbal forms, bad speciation, etc. • Bell Curve, balance of depth and width • Informal: An understandable organization of content that enables people to find information and which supports knowledge discovery. • Creates a context within which facts are related • Find, Identify, Describe information, relations, context

8. Content Management and Taxonomy: What? • Taxonomy as part of knowledge organization • Metadata: Dublin Core+ • CM functions: Language, Identifier, Rights • Combination functions: Publisher, Author • Subject matter functions: keywords, descriptions • Minimum need controlled vocabularies • Contextual • DocumentObjectType, AudienceType • Facets and entities • People, Companies, Compounds, Geography • Multiple views into content • Dynamic Mapping of facets

9. Content Management and Taxonomy: Why? • Search Stinks • Integrated Browse and Search works better than search • Ecommerce – 56% of all searches fail = lost income • Intranet = lost time, lost business, lost ideas • Taxonomic CM - Rich semantic web of concepts, not a unstructured collection of documents • Cost of poor Search and Content Management • If its not organized,you can’t find it. • If you can’t find it, you can’t use it. • If you can’t find it, you waste a lot of time. • If you can’t find it, you could lose an account. • If you can’t find it, you could look stupid. • If you can’t find it, it doesn’t exist.

10. Content Management and Taxonomy: Why? • In 2 years, categorization will replace search • Categorization will be a component/foundation for: • Search, content management, portals, CRM, collaboration, etc. • Beyond browse • Agent profiles – just in time news • Intelligent agents – semantic web • Contextualized search results • Personalization within communities

11. Content Management and Taxonomy: How? • Old Answer: Manual • hire a bunch of librarians and IA’s • Costly, difficult to maintain • Use SME’s • Costly, difficult to maintain, bad track record • New Answer: • Integrate Manual and new software • Integrate Content Management and Taxonomy • Integrate central team and local authors

12. Content Management and Taxonomy: How? • New Technologies • Unstructured Data Management • Taxonomy Management • Smart Categorization, summarization • Entity Extraction and metadata generation • Visualization of taxonomic relationships • Linguistic analysis, not just bag of words

13. Machine-Categorization: Methods • Semi-Automatic: Rules, If-Then • Maximum precision & flexibility • Catalog by Example: Bayesian, SVM, Neural • Training Sets (5-500) • Speed, Learning • Statistical Clustering • Set of Documents & Taxonomy Level • Semantic Analysis & World Knowledge

14. Machine Categorization: The Human Element • Automatic Categorization is Not • Humans are better, but not as consistent • Bring outside contexts to the document • Purpose, similar documents, common sense • Understandable mistakes • Computers are faster and cheaper • Categorization is part of knowledge organization • Meta data, communities, taxonomies, etc. • The Best Answer is Hybrid or Cyborg Categorization

15. Taxonomic Content Management: Standard View

16. Taxonomic Content Management: Taxonomic View Taxonomies: Content, Communities, Tasks Categorization MetaData

17. Taxonomic Content Management: Work Flow with Meaning • Preliminary Foundation Work • Design the ontology • Develop taxonomies • Design metadata standards • Collaborative development of controlled vocabularies • Authors, SME’s – check document in: • Have a summary either written by human or software • List of metadata suggestions, entities – people, places, etc. • Provisional categorization • Decision: publish or submit for review, central team or community of experts. • Request for additional keywords or categorization issues

18. Taxonomic Content Management: Work Flow with Meaning • Central Team • Review documents – easier, faster • Use summaries, metadata, entities to provide context • Review infrastructure requests – new keywords, categories • Integrated Work Flow • Strengths of local and central • Variety of roles, flexible (few dedicated roles needed). • Collaborative categorization and keywords by SME, software, and central team • SME’s can function as central team

19. Taxonomic Content Management: Work Flow with Meaning • Publish by Category, not web site • Web site is a terrible unit of organization of content • 10 to 10,000 documents • Who published is only one dimension • Flexible & Intelligent Publishing • Collaboration supported across organization • Dynamically generate views, facets, web sites • Supports intelligent personalization • Requires metadata to go beyond idiosyncratic views of content • Prompt on unusual connections • Pre-existing, categories • Regulatory or legal issue

20. Taxonomic Content Management: Work Flow with Meaning • Content Reorganization • Category + Publisher = related document sets • Rich web of related content • Content + background contexts • Legal/Policy contexts • Technical contexts • Customer / Task contexts • Support browse by topic, type, task, entity, facets

21. Taxonomic Content Management: Work Flow with Meaning • Design even more important • Taxonomic effort • Balance of pre-defined and dynamic • Broader context of content, communities, processes • CM companies are developing or buying taxonomic capabilities • Metadata, categorization, summarization, etc. • CM as a platform technology • Article – EContent October – KM and E-Learning • CM, LCMS, LMS, KM platform • CM: Beyond Categorization • Collaboration: E-Room, Intraspect • Search and Portals: Epicentric • CM as part of Intellectual Infrastructure

22. Infrastructure Content Management Technology, Teams, Taxonomies • Technology • CM -- Least important – unless you get it wrong • Taxonomic Software • Support articulation of intellectual infrastructure • Integrated with CM – supports maintenance • KM Platform – CM in Context • Search, Portals, Collaboration • Supports application of the intellectual infrastructure

23. Infrastructure Content Management Technology, Teams, Taxonomies • Teams – Where? • Best: Central, Dedicated Department • Cross Organizational, Multidisciplinary • Part Time, Distributed SME’s, Business owners • Practical, real world input • Partners: IT, HR, Corporate Communication, Library, Training • Worst: IT Project Manger, Intranet programming team

24. Infrastructure Content Management Technology, Teams, Taxonomies • Teams – Who? • Knowledge Architect and Learning Object Designers • Knowledge Engineers and curriculum developers • Knowledge Facilitators and Trainers • IT, Web developers, application programmers • Librarians and information architects • Business analysts and project managers • Corporate Communication writers and editors

25. Infrastructure Content Management Technology, Teams, Taxonomies • Teams – What? • Infrastructure Activities • Integrate taxonomy across the company • Content, communities, activities • Grow and Develop taxonomies • Taxonomy metrics require skill to fix problems • 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 • Cognitive Difference – Geography of Thought • Panda, monkey, banana

26. Infrastructure Content Management Technology, Teams, Taxonomies • Taxonomies and Beyond: • Intellectual infrastructure – Context for CM • Taxonomy of Communities • Map of formal and informal communities • Social Network Analysis, Personas • Community specific vocabularies • Integrate with knowledge objects, metadata • Expertise Location, mentoring, story telling • Communities of Practice • Training • Embedded Learning - Just-In-Time, Performance Support

27. Infrastructure Content Management Technology, Teams, Taxonomies • Taxonomies and Beyond: • Document not the best unit of organization in all situations • Learning/Knowledge Objects • Chunks of content and XML metadata • Reusable, flexible, answer machines • Important of context – rules for relating objects • Advanced MetaData • SCORM+ • Semantic Density, typical learning time • RDF and Semantic Web • subClassOf, seeAlso, isRelatedTo

28. ICM and Applications: Contextualizing Content • Knowledge Creating • Innovation, E-learning, LMS • Collaboration • Distributed Categorization, Community Vocabularies • Knowledge Sharing / Transmission • Collaboration, Retrieval – content and experts • Knowledge Using • Smart Applications, Portals • CRM, Data warehouse, text mining, business intelligence

29. ICM and Applications: Contextualizing Content • Knowledge = information + contexts • Contexts are what gives depth and meaning to information • Let me tell you a story • Contextualizing Content • Related topics, contexts, content types • Rules for relating, integrating contexts

30. Knowledge Retrieval: Contexts • Search for product name • List of documents that are explicitly about the product • Category Views • Features of the product • Product comparisons • Legal or policy documents • Background Resources • List of Experts, communities • Glossaries, internal libraries

31. Knowledge Retrieval: Contexts • Search for product name • Search and Browse options • Text or visual options • Offers a variety of contexts: • Related content, best bests (community based and input form central team) • Learns from my behavior and community behavior • Usage Analytics – based on meaning, not counting clicks

32. Knowledge Retrieval: Contexts • Search for product name • Filters • Admin in retail tech support • Belong to a discussion group • Last time I looked up product information, I looked at certain documents and types • I don’t want legal information emphasized and I’m not an expert on this product

33. Summary • Successful Content Management requires a taxonomic dimension • CM companies have recognized this and added features • Next Step: Content management as infrastructure platform • Need: well articulated intellectual infrastructure • 3 important terms: Contexts, Taxonomies, Intellectual Infrastructure • Your choice – go back to file management or forward to infrastructure content management

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