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Enterprise Semantic Infrastructure Workshop . Tom Reamy Chief Knowledge Architect KAPS Group Knowledge Architecture Professional Services http://www.kapsgroup.com. Agenda. Introduction Semantic Infrastructure Basic Concepts – Content, People, Business Processes, Technology

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enterprise semantic infrastructure workshop

Enterprise Semantic Infrastructure Workshop

Tom ReamyChief Knowledge Architect

KAPS Group

Knowledge Architecture Professional Services

http://www.kapsgroup.com

agenda
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
kaps group general
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

Semantic InfrastructureBasic Concepts & Benefits

Tom ReamyChief Knowledge Architect

KAPS Group

Knowledge Architecture Professional Services

http://www.kapsgroup.com

agenda1
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
semantic infrastructure 4 dimensions
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
semantic infrastructure 4 dimensions content and content structure
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
knowledge structures
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
a framework of knowledge structures
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
semantic infrastructure people
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
semantic infrastructure dimensions people central team
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
semantic infrastructure dimensions people location of team
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
semantic infrastructure dimensions technology infrastructure
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
infrastructure solutions resources technology
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
infrastructure solutions elements business processes
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
semantic infrastructure the start and foundation knowledge architecture audit
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
semantic infrastructure the start and foundation knowledge architecture audit1
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
semantic infrastructure the start and foundation knowledge architecture audit2
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
semantic infrastructure enterprise taxonomies wrong approach
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
semantic infrastructure content structures new approach
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
semantic infrastructure design people technology business processes
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
semantic infrastructure benefits why semantic infrastructure
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
semantic infrastructure benefits general time and productivity
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
semantic infrastructure benefits return on existing technology
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
semantic infrastructure benefits return on existing technology1
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
semantic infrastructure benefits infrastructure vs projects
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?
semantic infrastructure benefits knowledge management benefits
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)
semantic infrastructure benefits selling the benefits
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
conclusion
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

Questions?

Tom [email protected]

KAPS Group

Knowledge Architecture Professional Services

http://www.kapsgroup.com

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