Transcript Taxonomy Development Workshop

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
– Developing an Articulated Strategic Vision
– Benefits of an Infrastructure Approach
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 Development and Maintenance of a Semantic Infrastructure
Semantic Tools – Capabilities & Acquisition Strategy
– Development Processes & Best Practices
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 Semantic Infrastructure Applications
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Enterprise Search
– Search Based Applications & Beyond
 Discussion &Questions / Lunch
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KAPS Group: General
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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
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Questions?
Tom Reamy
[email protected]
KAPS Group
Knowledge Architecture Professional Services
http://www.kapsgroup.com
Semantic Infrastructure
Basic Concepts & Benefits
Tom Reamy
Chief Knowledge Architect
KAPS Group
Knowledge Architecture Professional Services
http://www.kapsgroup.com
Agenda
 Semantic Infrastructure – Basic Concepts
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Content & Content Structure
– People – Resources, Producers, Consumers
– Semantics in Business Processes
– Technology – Information, Text Analytics, Text Mining
 Semantic Infrastructure – Strategic Foundation
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Knowledge Audit Plus
 Semantic Infrastructure – Benefits of an Infrastructure Approach
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Infrastructure vs. Projects
Semantics vs. Technology
 Conclusion
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Semantic Infrastructure: 4 Dimensions
 Ideas – Content and Content Structure
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Map of Content – Tribal language silos
Structure – articulate and integrate
 People – Producers & Consumers
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Communities, Users, Central Team
 Activities – Business processes and procedures
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Semantics, information needs and behaviors
 Technology
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CMS, Search, portals, text analytics
Applications – BI, CI, Semantic Web, Text Mining
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Semantic Infrastructure: 4 Dimensions
Content and Content Structure
 Map multiple types and sources of content
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Structured and unstructured, internal and external
 Beyond Metadata and Taxonomy
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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
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Model of framework and smart modules
 Framework
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Faceted metadata
Simple taxonomies with intelligence – categorization & extraction
Ontology and Semantic Web
Best bets and user metadata
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Knowledge Structures
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List of Keywords (Folksonomies)
Controlled Vocabularies, Glossaries
Thesaurus
Browse Taxonomies (Classification)
Formal Taxonomies
Faceted Classifications
Semantic Networks / Ontologies
Categorization Taxonomies
Topic Maps
Knowledge Maps
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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
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Semantic Infrastructure: People
 Communities / Tribes
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Different languages
– Different Cultures
– Different models of knowledge
 Two needs – support silos and inter-silo communication
 Types of Communities
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Formal and informal
Variety of subject matters – vaccines, research, sales
Variety of communication channels and information behaviors
 Individual People – tacit knowledge / information behaviors
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Consumers and Producers of information – In Depth
Map major types
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Semantic Infrastructure Dimensions
People: Central Team
 Central Team supported by software and offering services
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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
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Semantic Infrastructure Dimensions
People: Location of Team
 KM/KA Dept. – Cross Organizational, Interdisciplinary
 Balance of dedicated and virtual, partners
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Library, Training, IT, HR, Corporate Communication
 Balance of central and distributed
 Industry variation
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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
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Semantic Infrastructure Dimensions
Technology Infrastructure
 Enterprise platforms: from creation to retrieval to
application
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Semantic Infrastructure as the computer network
• Applications – integrated meaning, not just data
 Semantic Structure
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Text Analytics – taxonomy, categorization, extraction
 Integration Platforms – Content management, Search
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Add structure to content at publication
Add structure to content at consumption
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Infrastructure Solutions: Resources
Technology
 Text Mining
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Both a structure technology – taxonomy development
And an application
 Search Based Applications
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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)
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When, who, how, and how much structure to add
– Workflow with meaning, distributed subject matter experts (SMEs)
and centralized teams
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Infrastructure Solutions: Elements
Business Processes
 Platform for variety of information behaviors & needs
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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
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Enhance Both Efficiency and Quality
 Enhance support processes – education, training
 Develop new processes and capabilities
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External Content – Text mining, smarter categorization
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Semantic Infrastructure: The start and foundation
Knowledge Architecture Audit
 Knowledge Map - Understand what you have, what you
are, what you want
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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
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Knowledge Architecture Audit:
Knowledge Map
Project
Foundation
Contextual
Interviews
Information
Interviews
App/Content User Survey
Catalog
Strategy
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
Deep
Details
Complete
Picture
New
Foundation
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Semantic Infrastructure
Enterprise Taxonomies: Wrong Approach
 Very difficult to develop - $100,000’s
 Even more difficult to apply
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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
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Tyranny of the majority, madness of crowds
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Semantic Infrastructure
Content Structures: New Approach
 Simple Subject Taxonomy structure
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Easy to develop and maintain
 Combined with categorization capabilities
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Added power and intelligence
 Combined with Faceted Metadata
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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
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Multiple applications – Text mining to Search
Combine search and browse
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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
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Hybrid model – combination of automatic and human
 Business Processes – integrated search with activities, text
analytics based applications , intelligent routing
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Semantic Infrastructure Benefits
Why Semantic Infrastructure
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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?
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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
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Semantic Infrastructure Benefits
General Time and Productivity
 Time Savings – Too Big to Believe?
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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
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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
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Semantic Infrastructure Benefits
Return on Existing Technology
 Enterprise Content Management - $100K - $2M
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Underperforming – year after year, new initiative every 5 years
 ECM as part of a Platform
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Enhance search – improved metadata, especially keywords
 A Hybrid Model of ECM and Metadata
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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
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Semantic Infrastructure Benefits
Return on Existing Technology
 Enterprise Search - $100K - $2M
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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
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Semantic Infrastructure Benefits
Infrastructure vs. Projects
 Strategic foundation vs. Short Term
 Integrated solution – CM and Search and Applications
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Better results
Avoid duplication
 Semantics
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Small comparative cost
Needed to get full value from all the above
 ROI – asking the wrong question
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What is ROI for having an HR department?
What is ROI for organizing your company?
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Semantic Infrastructure Benefits
Selling the Benefits
 CTO, CFO, CEO
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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
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Conclusion
 Semantic Infrastructure is not just a project
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Foundation and Platform for multiple projects
 Semantic Infrastructure is not just about search
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It is about language, cognition, and applied intelligence
 Strategic Vision (articulated by K Map) is essential
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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
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Initial Project = +10%, All Other Projects = -50%
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Questions?
Tom Reamy
[email protected]
KAPS Group
Knowledge Architecture Professional Services
http://www.kapsgroup.com