Enterprise Semantic Infrastructure Workshop Tom Reamy Chief Knowledge Architect KAPS Group Knowledge Architecture Professional Services

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

Semantic Infrastructure Basic Concepts & Benefits Tom Reamy Chief Knowledge Architect KAPS Group Knowledge Architecture Professional Services

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 Dimensions People: 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 Dimensions People: 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 Dimensions Technology 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: Resources Technology  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: Elements Business 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 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, 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 foundation Knowledge 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 foundation Knowledge 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 Project Foundation Contextual Interviews Information Interviews App/Content Catalog User SurveyStrategy Document Meetings, work groups Overview High Level: Process Community Info behaviors of Business processes Technology and content All 4 dimensions Meetings, work groups General Outline Broad Context Deep Details Complete Picture New Foundation

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 Benefits Why 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 Benefits General 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 Benefits Return 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 Benefits Return 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 Benefits Infrastructure 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 Benefits Knowledge 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 Benefits Selling 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%

Questions? Tom Reamy KAPS Group Knowledge Architecture Professional Services