Metadata Strategy
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Transcript Metadata Strategy
Search, Browse, and Faceted
Navigation
Tom Reamy
Chief Knowledge Architect
KAPS Group
Knowledge Architecture Professional Services
http://www.kapsgroup.com
Agenda
Introduction
Essentials of Facets / Faceted Navigation
Facets in Government / Enterprise
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Differences
Basic Design of Search / Browse / Facets
Case Studies – Tale of Two Taxonomies
Search / Browse / Facets – Web 2.0 & Future Trends
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KAPS Group: General
Knowledge Architecture Professional Services
Virtual Company: Network of consultants – 12-15
Partners – FAST/Convera, Inxight, SchemaLogic, etc.
Consulting, Strategy, Knowledge architecture audit
Taxonomies: Enterprise, Marketing, Insurance, etc.
Services:
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Taxonomy development, consulting, customization
Technology Consulting – Search, CMS, Portals, etc.
Metadata standards and implementation
Knowledge Management: Collaboration, Expertise, e-learning
Applied Theory – Faceted taxonomies, complexity theory, natural
categories
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History of Facets
S. R. Ranganathan – 1960’s (Taxonomies – Aristotle)
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Issue of Compound Subjects
– The Universe consists of PMEST
• Personality, Matter, Energy, Space, Time
Classification Research Group- 1950’s, 1970’s
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Facet analysis as basis for all bibliographic classifications
Based on Ranganathan, simplified
Principles:
• Division – a facet must represent only one characteristic
• Mutual Exclusivity
– More flexible, less doctrinaire
Classification Theory to Web Implementation
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An Idea waiting for a technology - Multiple Filters / dimensions
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Essentials of Facets
Facets are not categories
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Entities or concepts belong to a category
– Entities have facets
Facets are metadata - properties or attributes
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Entities or concepts fit into one or more categories
All entities have all facets – defined by set of values
Facets are orthogonal – mutually exclusive – dimensions
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An event is not a person is not a document is not a place.
Facets – variety – of units, of structure
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Numerical range (price), Location – big to small
Alphabetical, Hierarchical - taxonomic
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Essentials of Faceted Navigation
Not a Yahoo-style Browse
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Computer Stores under Computers and Internet
– One value per facet per entity
Faceted Navigation
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Facets are filters, multidimensional
– Browse within a facet, filter by multiple facets
Facets are applied at search time – post-coordination, not precoordination [Advanced Search]
Faceted Navigation is an active interface – dynamic combination
of search and browse
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Faceted Navigation: Advantages
More intuitive – easy to guess what is behind each door
• Simplicity of internal organization
• 20 questions – we know and use
Dynamic selection of categories
• Allow multiple perspectives/ no universal set needed
• Ability to Handle Compound Subjects
Trick Users into “using” Advanced Search
• wine where color = red, price = x-y, etc.
• Click on color red, click on price x-y, etc.
Systematic Advantages:
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Need fewer Elements
– 4 facets of 10 nodes = 10,000 node taxonomy
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Faceted Navigation: Disadvantages
Lack of Standards for Faceted Classifications
• Every project is unique customization
Difficulty of expressing complex relationships
• Simplicity of internal organization
Loss of Browse Context
• Difficult to grasp scope and relationships
Essential Limit of Faceted Navigation
Limited Domain Applicability – type and size
– Cost of tagging
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Trade off between simplicity (power and ease of understanding)
and complexity (real world)
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Government & Enterprise Environment
Agency Content – different world than eCommerce
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More Content, more kinds, more unstructured
– Not a catalog to start – less metadata and structured content
– Complexity -- not just content but variety of users and activities
Agency – Question of Balance / strategy
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More facets = more findability (up to a point)
– Fewer facets = lower cost to tag documents
Facet structures are more complex than in eCommerce
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Multiple structures, more subject like
Need to start with major research (KA Audit)
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Content, users, business activities, information technologies
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Knowledge Architecture Audit:
Knowledge Map
Project
Foundation
Contextual
Interviews
Information
Interviews
App/Content User Survey
Catalog
Knowledge
Map
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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Facets, Search, Browse
Enterprise Design Issues - General
How many Facets do you need?
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“Can’t we start with just 1 or 2 facets and see how it works?”
Balance of metadata overhead, findability, personalization
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Distributed model reduces cost – enables more facets
ECM – publishing process, policy
Distributed taggers – users, user communities (2.0), KM-Library
Auto Populate – Organization, Location
Software – entity extraction, summarization, auto-categorization
Rule of Thumb:
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Small catalog of homogenous items 3-4
Enterprise content – 4-8
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Enterprise Environment – Case Studies
A Tale of Two Taxonomies
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It was the best of times, it was the worst of times
Basic Approach
Initial meetings – project planning
– High level K map – content, people, technology
– Contextual and Information Interviews
– Content Analysis
– Draft Taxonomy – validation interviews, refine
– Integration and Governance Plans
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Enterprise Environment – Case One – Taxonomy, 7 facets
Taxonomy of Subjects / Disciplines:
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Science > Marine Science > Marine microbiology > Marine toxins
Facets:
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Organization > Division > Group
– Clients > Federal > EPA
– Instruments > Environmental Testing > Ocean Analysis > Vehicle
– Facilities > Division > Location > Building X
– Methods > Social > Population Study
– Materials > Compounds > Chemicals
– Content Type – Knowledge Asset > Proposals
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Enterprise Environment – Case One – Taxonomy, 7 facets
Project Owner – KM department – included RM, business
process
Involvement of library - critical
Realistic budget, flexible project plan
Successful interviews – build on context
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Overall information strategy – where taxonomy fits
Good Draft taxonomy and extended refinement
Software, process, team – train library staff
– Good selection and number of facets
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Final plans and hand off to client
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Enterprise Environment – Case Two – Taxonomy, 4 facets
Taxonomy of Subjects / Disciplines:
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Geology > Petrology
Facets:
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Organization > Division > Group
Process > Drill a Well > File Test Plan
Assets > Platforms > Platform A
Content Type > Communication > Presentations
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Enterprise Environment – Case Two – Taxonomy, 4 facets
Location – not KM – tied to RM and software
• Solution looking for the right problem
• No Library or Training involvement
Value of taxonomy understood, but not the complexity and scope
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Under budget, under staffed
Not enough research – and wrong people
Not enough facets
Wrong set of facets – business not information
– Ill-defined facets – too complex internal structure
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Wrong kind of project management
• Special needs of a taxonomy project
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Facets and 2.0
“It’s MySpace meets YouTube meets Wikipedia meets Google –
on steroids.”
“It’s ignorance meets egotism meets bad taste meets mob rule –
on steroids.” – The Cult of the Amateur – Andrew Keen
Revolution and Evolution
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Doesn’t anyone do evolution (Web 1.2 anyone?)
Wikipedia – users can do it all - NOT
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With the help of 2,000 trusted editors and software, combating the
passionate conviction and impact of money
Wisdom of Crowds
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Good for guessing jelly beans, not useful tags
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Folksonomies – Good and Bad
Advantages
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Simple, Lower cost of categorization
Can respond quickly to changes, User’s own terms
Better than no tags at all (Not really)
Getting people excited about metadata!
Disadvantages
They don’t work very well for finding
No structure, no conceptual relationships
Quality and Popularity are very different
Issues of scale – popular tags already showing a million hits
– Errors – misspellings, single words or bad compounds, single use or
idiosyncratic use
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Social mechanism – opposite of wisdom of crowds
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Tyranny of the majority
Del.icio.us – Design – 1 Mil (computer design)
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Facets and 2.0 – Evolving answers
Technology
Integrated Evolving Solution: Technology, People, Semantics //
with Feedback with consequences
Enterprise Content Management
Place to add metadata – of all kinds, not just keywords
Policy support – important, part of job performance
– Add tag clouds to input page
– More sophisticated displays
• Tag clouds mapped to community map
• Tag clusters, taxonomy location
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Semantic Software – Inxight, Teragram, etc.
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Suggest terms based on text, on tag clouds
Enterprise Search
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Search – Browse – Facets
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Facets and 2.0 – Evolving answers
People
New Relationship of Center and Crowd
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Not top down or bottom up
– More sophisticated support, more freedom, more suggestions, more
user input
– - New roles – for users (taggers, part of variety of communities –
both distributed and central)
– New roles for central – create feedback system, tweak the evolution
of the system, Develop initial candidates
Communities of Practice – apply to tagging, ranking
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Community Maps – formal and informal
Map tags to communities – more useful suggestions
Use tags to uncover communities
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Facets and 2.0 – Evolving answers
Semantics
Start and end with a formal taxonomy / Ontology
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Findability vastly superior
– Communication with others – share tags
– Take advantage of conceptual relationships
Tagging experience – folksonomies plus
Users can type any word – system looks it up – plurals, synonyms,
preferred terms, spelling variations
– Software suggestions – based on content of bookmark, document
and on popular user tags
• Cognitively simpler task than own value, complex hierarchy
– New terms flagged and routed to central team
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Feedback with consequences
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Rank quality of tags, quality of taggers
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Facets: Future Trends
Facets and Facts / Ontologies
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Types of relationships: People have friends, family, bosses and
employees, jobs
– Implications of those relationships – doctor has patients, salesman
has customers
– Facets are a foundation for precise rules and relationships
• Define important types of relationships for each facet dimension.
Advanced Applications – Text and Data Mining, Alerts
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Combining Subject Matter and Topical Facets
Map Topics and Facets
• Quality control for drilling new well in region X
– Rules – Contains any of type x entity or facet (products), plus
complex conceptual content, plus certain values within a facet
(buying activity), then send alert
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Conclusions: Facets not Folksonomies
Facets are an important addition to Search / Browse
Facets require adding lots of meta data – and that is a good thing
Facets require that you understand your users – and that is a
good thing
Facets support the range of Government users – dynamic
personalization – multiple interests, multiple info behaviors
An integrated search-browse-facet user interface provides simple
complexity
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supports both quick answers to specific questions and deep research
exploration
You want a revolution? Integrate 2.0 with meaning (3.0)
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Dynamic dimensions – User and semantics
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Questions?
Tom Reamy
[email protected]
KAPS Group
Knowledge Architecture Professional Services
http://www.kapsgroup.com