Enterprise Semantic Infrastructure Workshop

1. Enterprise Semantic Infrastructure Workshop Tom ReamyChief Knowledge Architect KAPS Group Knowledge Architecture Professional Services http://www.kapsgroup.com

2. Agenda • Introduction • Semantic Infrastructure • Basic Concepts – Content, People, Business Processes, Technology • Developing an Articulated Strategic Vision • Benefits of an Infrastructure Approach • Development and Maintenance of a Semantic Infrastructure • Semantic Tools – Capabilities & Acquisition Strategy • Development Processes & Best Practices • Semantic Infrastructure Applications • Enterprise Search • Search Based Applications & Beyond • Discussion &Questions

3. KAPS Group: General • Knowledge Architecture Professional Services • Virtual Company: Network of consultants – 8-10 • Partners – SAS, Smart Logic, Microsoft, Concept Searching, etc. • Consulting, Strategy, Knowledge architecture audit • Services: • Taxonomy/Text Analytics development, consulting, customization • Technology Consulting – Search, CMS, Portals, etc. • Evaluation of Enterprise Search, Text Analytics • Metadata standards and implementation • Knowledge Management: Collaboration, Expertise, e-learning • Applied Theory – Faceted taxonomies, complexity theory, natural categories

4. Semantic InfrastructureBasic Concepts & Benefits Tom ReamyChief Knowledge Architect KAPS Group Knowledge Architecture Professional Services http://www.kapsgroup.com

5. Agenda • Semantic Infrastructure – Basic Concepts • Content & Content Structure • People – Resources, Producers, Consumers • Semantics in Business Processes • Technology – Information, Text Analytics, Text Mining • Semantic Infrastructure – Strategic Foundation • Knowledge Audit Plus • Semantic Infrastructure – Benefits of an Infrastructure Approach • Infrastructure vs. Projects • Semantics vs. Technology • Conclusion

6. Semantic Infrastructure: 4 Dimensions • Ideas – Content and Content Structure • Map of Content – Tribal language silos • Structure – articulate and integrate • People – Producers & Consumers • Communities, Users, Central Team • Activities – Business processes and procedures • Semantics, information needs and behaviors • Technology • CMS, Search, portals, text analytics • Applications – BI, CI, Semantic Web, Text Mining

7. Semantic Infrastructure: 4 Dimensions Content and Content Structure • Map multiple types and sources of content • Structured and unstructured, internal and external • Beyond Metadata and Taxonomy • Keywords - poor performance • Dublin Core: hard to implement • Dublin Core: Too formal and not formal enough • Need structures that are more powerful and more flexible • Model of framework and smart modules • Framework • Faceted metadata • Simple taxonomies with intelligence – categorization & extraction • Ontology and Semantic Web • Best bets and user metadata

8. Knowledge Structures • List of Keywords (Folksonomies) • Controlled Vocabularies, Glossaries • Thesaurus • Browse Taxonomies (Classification) • Formal Taxonomies • Faceted Classifications • Semantic Networks / Ontologies • Categorization Taxonomies • Topic Maps • Knowledge Maps

9. A Framework of Knowledge Structures • 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, Categorization Taxonomies • Applications • Level 4 – Knowledge maps • Strategic Resource

10. Semantic Infrastructure: People • Communities / Tribes • Different languages • Different Cultures • Different models of knowledge • Two needs – support silos and inter-silo communication • Types of Communities • Formal and informal • Variety of subject matters – vaccines, research, sales • Variety of communication channels and information behaviors • Individual People – tacit knowledge / information behaviors • Consumers and Producers of information – In Depth • Map major types

11. Semantic Infrastructure DimensionsPeople: Central Team • Central Team supported by software and offering services • Creating, acquiring, evaluating taxonomies, metadata standards, vocabularies, categorization taxonomies • 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 • Facilitate knowledge capture in projects, meetings

12. Semantic Infrastructure DimensionsPeople: 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

13. Semantic Infrastructure DimensionsTechnology Infrastructure • Enterprise platforms: from creation to retrieval to application • Semantic Infrastructure as the computer network • Applications – integrated meaning, not just data • Semantic Structure • Text Analytics – taxonomy, categorization, extraction • Integration Platforms – Content management, Search • Add structure to content at publication • Add structure to content at consumption

14. Infrastructure Solutions: ResourcesTechnology • Text Mining • Both a structure technology – taxonomy development • And an application • Search Based Applications • Portals, collaboration, business intelligence, CRM • Semantics add intelligence to individual applications • Semantics add ability to communicate between applications • 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

15. Infrastructure Solutions: ElementsBusiness Processes • Platform for variety of information behaviors & needs • Research, administration, technical support, etc. • Types of content, questions • Subject Matter Experts – Info Structure Amateurs • Web Analytics – Feedback for maintenance & refine • Enhance Basic Processes – Integrated Workflow • Enhance Both Efficiency and Quality • Enhance support processes – education, training • Develop new processes and capabilities • External Content – Text mining, smarter categorization

16. Semantic Infrastructure: 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, Text Mining • 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. Semantic Infrastructure: The start and foundationKnowledge Architecture Audit • Phase I • Initial Discussion, Plan • Get high level structure, inventory of content • Get high level business, organization, technology structure • Onsite – 1 day to 1 week • Planning meetings, general contextual info • Get access to content – documents, databases, spider • Decide who to talk to and get access to them

18. Semantic Infrastructure: The start and foundationKnowledge Architecture Audit • Phase II • Spider Content • Explore content – text mining, clusters, categorization, etc. • Work sessions – SME’s, feedback in initial structures • Interviews – SME’s – work flow, info in business processes • Survey – optional – broad look at interview info • Phase III • Develop K Map – ontologies, taxonomies, categorization • Train K Map – questions, feedback • Develop Expertise Map, Other Maps // Train • Final Strategy Report and K Map

19. Knowledge Architecture Audit:Knowledge Map

20. Semantic Infrastructure Enterprise Taxonomies: Wrong Approach • Very difficult to develop - $100,000’s • Even more difficult to apply • Teams of Librarians or Authors/SME’s • Cost versus Quality • Problems with maintenance • Cost rises in proportion with granularity • Difficulty of representing user perspective • Social media requires a framework – doesn’t create one • Tyranny of the majority, madness of crowds

21. Semantic Infrastructure Content Structures: New Approach • Simple Subject Taxonomy structure • Easy to develop and maintain • Combined with categorization capabilities • Added power and intelligence • Combined with Faceted Metadata • Dynamic selection of simple categories • Allow multiple user perspectives • Can’t predict all the ways people think • Monkey, Banana, Panda • Combined with ontologies and semantic data • Multiple applications – Text mining to Search • Combine search and browse

22. Semantic Infrastructure Design: People, Technology, Business Processes • People (Central) – tagging, evaluating tags, fine tune rules and taxonomy • People (Users) - social tagging, suggestions • Software - Text analytics, auto-categorization, entity extraction • Software – Search, Content Management, Portals-Intranets • Hybrid model – combination of automatic and human • Business Processes – integrated search with activities, text analytics based applications , intelligent routing

23. Semantic Infrastructure BenefitsWhy Semantic Infrastructure • Unstructured content = 80% or more of all content • Limited Usefullness – database of unstructured content • Need to add (infra) structure to make it useful • Information is about meaning, semantics • Search is about semantics, not technology • Can’t Google do it? • Link Algorithm – human act of meaning • Doesn’t work in enterprise • 1,000’s of editors adding meaning • New technology makes it possible – Text Analytics

24. Semantic Infrastructure BenefitsGeneral Time and Productivity • Time Savings – Too Big to Believe? • Lost time searching - $12M a year per 1,000 • Cost of recreating lost information - $4.5M per 1,000 • Cost of not finding the right information – Years? • 10% improvement = $1.2M a year per 10,000 • Making Metrics Human • Number of addition FTE’s at no cost (enhanced productivity) • Savings passed on to clients • Spreadsheet of extra activities (ex. Training – working smarter • Build a more integrated, smarter organization

25. Semantic Infrastructure BenefitsReturn on Existing Technology • Enterprise Content Management - $100K - $2M • Underperforming – year after year, new initiative every 5 years • ECM as part of a Platform • Enhance search – improved metadata, especially keywords • A Hybrid Model of ECM and Metadata • Authors, editors-librarians, Text Analytics • Submit a document -> TA generates metadata, extracts concepts, Suggests categorization (keywords) -> author OK’s (easy task) -> librarian monitors for issues • Use results as input into analytics

26. Semantic Infrastructure BenefitsReturn on Existing Technology • Enterprise Search - $100K - $2M • Cost Effective and good quality keywords / categorization • More metadata – faceted navigation • Work with ECM or dynamically generate categorization at search results time • Rich results – summaries, categorization, facets like date, people, organizations, etc. Tag clouds and related topics • Foundation for Search Based Applications – all need semantics

27. Semantic Infrastructure BenefitsInfrastructure vs. Projects • Strategic foundation vs. Short Term • Integrated solution – CM and Search and Applications • Better results • Avoid duplication • Semantics • 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?

28. Semantic Infrastructure BenefitsKnowledge Management Benefits • Foundation for advanced knowledge representations • Capture the depth and complexity of knowledge context • Connect KM initiatives to entire organization • Information AND Knowledge (and Data) • CIO resources with KM depth • Foundation for KM initiatives that work and deliver value • Portals and Expertise and Communities • New KM initiatives – combine sophisticated handling of language and knowledge and education • Return knowledge to knowledge management • Cognitive Science could change everything (almost)

29. Semantic Infrastructure BenefitsSelling the Benefits • CTO, CFO, CEO • Doesn’t understand – wrong language • Semantics is extra – harder work will overcome • Not business critical • Not tangible – accounting bias • Does not believe the numbers • Believes he/she can do it • Need stories and figures that will connect • Need to understand their world – every case is different • Need to educate them – Semantics is tough and needed

30. Conclusion • Semantic Infrastructure is not just a project • Foundation and Platform for multiple projects • Semantic Infrastructure is not just about search • It is about language, cognition, and applied intelligence • Strategic Vision (articulated by K Map) is essential • Even for your under the radar vocabulary project • Paying attention to theory is practical • Benefits are enormous – believe it! • Think Big, Start Small, Scale Fast • Initial Project = +10%, All Other Projects = -50%

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