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Enterprise Semantic Infrastructure Workshop
Tom ReamyChief Knowledge Architect
KAPS Group
Knowledge Architecture Professional Services
http://www.kapsgroup.com
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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 / Lunch
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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
Questions?
KAPS Group
Knowledge Architecture Professional Services
http://www.kapsgroup.com
Semantic InfrastructureBasic Concepts & Benefits
Tom ReamyChief Knowledge Architect
KAPS Group
Knowledge Architecture Professional Services
http://www.kapsgroup.com
6
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
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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
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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
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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
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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– 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
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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
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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
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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
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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
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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
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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
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Knowledge Architecture Audit:Knowledge MapProject Foundation
Contextual Interviews
InformationInterviews
App/ContentCatalog
User Survey StrategyDocument
Meetings, work groupsOverview
High Level:ProcessCommunity
Info behaviors of Business processes
Technology and content
All 4 dimensions
Meetings, work groups
General Outline
Broad Context
Deep Details
Deep Details
Complete Picture
NewFoundation
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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
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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
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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
– 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 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
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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
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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
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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
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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?
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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
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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?
KAPS Group
Knowledge Architecture Professional Services
http://www.kapsgroup.com
