1. Taxonomy Development Knowledge Structures
Tom Reamy Chief Knowledge Architect
KAPS Group
Knowledge Architecture Professional Services

2. Agenda Introduction Knowledge Structures Taxonomy Management Software
Exercises
Conclusion

3. Knowledge Structures List of Keywords (Folksonomies)
Controlled Vocabularies, Glossaries
Thesaurus
Browse Taxonomies (Classification)
Formal Taxonomies
Faceted Classifications
Semantic Networks / Ontologies
Topic Maps
Knowledge Maps

4. Knowledge Structures Lists of Keywords (Folksonomies)
Folksonomy (also known as collaborative tagging, social classification, social indexing, and social tagging) is the practice and method of collaboratively creating and managing tags to annotate and categorize content. Folksonomy describes the bottom-up classification systems that emerge from social tagging.[1] In contrast to traditional subject indexing, metadata is generated not only by experts but also by creators and consumers of the content. Usually, freely chosen keywords are used instead of a controlled vocabulary.[2] Folksonomy (from folk + taxonomy) is a user generated taxonomy.

5. Knowledge Structures Controlled Vocabularies, Glossaries
Lists with minimum structure
Easy to develop
Difficult to get value from
Simple Reference resource
Thesaurus
Taxonomy-like
Less formal
BT, NT – also RT

6. Two Types of Taxonomies: Browse and Formal Browse Taxonomy – Yahoo

7. Two Types of Taxonomies: Formal

8. Facets and Dynamic Classification
Facets are not categories
Entities or concepts belong to a category
Entities have facets
Facets are metadata - properties or attributes
Entities or concepts fit into one category
All entities have all facets – defined by set of values
Facets are orthogonal – mutually exclusive – dimensions
An event is not a person is not a document is not a place.
Facets – variety – of units, of structure
Date or price – numerical range
Location – big to small (partonomy)
Winery – alphabetical
Hierarchical - taxonomic

9. Knowledge Structures Semantic Networks / Ontologies
Ontology more formal
XML standards – OWL, DAML
Semantic Web – machine understanding
RDF – Noun – Verb – Object
Vice President is Officer
Build implications – from properties of Officer
Semantic Network – less formal
Represent large ontologies
Synonyms and variety of relationships

10. Knowledge Structures: Ontology
Instruments
Music
is a
is a
create
Bluegrass
Violins
uses
Musicians
uses
is a
Violinists

11. Knowledge Structures Topic Maps
ISO Standard
See
Topic Maps represent subjects (topics) and associations and occurrences
Similar to semantic networks
Ontology defines the types of subjects and types of relationships
Combination of semantic network and other formal structures (taxonomy or ontology)

12. Knowledge Structure: Topic Maps

13. Knowledge Structures Knowledge Maps
No standards – applied at high level
Ontologies plus / applied to specific environment
Map of Groups – Content Stores – Purpose – Technology
Add structure to each element
Facet Structure – filter by group – content – purpose
Strategic resource

14. Knowledge Structures: Which one to use?
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
Applications
Level 4 – Knowledge maps
Strategic Resource

15. Web 2.0 – No need for Taxonomies etc.?
“Tags are great because you throw caution to the wind, forget about whittling down everything into a distinct set of categories and instead let folks loose categorizing their own stuff on their own terms." - Matt Haughey - MetaFilter
Tyranny of the majority - worst type of central authority
More Madness of Crowds than Wisdom of Crowds
“Things fall apart; the center cannot hold; Mere anarchy is loosed upon the world,… The best lack all conviction, while the worst Are full of passionate conviction.” - The Second Coming – W.B. Yeats

16. Advantages of Folksonomies
Simple (no complex structure to learn)
No need to learn difficult formal classification system
Lower cost of categorization
Distributes cost of tagging over large population
Open ended – can respond quickly to changes
Relevance – User’s own terms
Support serendipitous form of browsing
Easy to tag any object – photo, document, bookmark
Better than no tags at all
Getting people excited about metadata!

17. Folksonomies – Problems and Limits
Folksonomies don’t compare with taxonomies or ontologies
Serendipity browsing is small part of search
Limited areas of success – popular sites are popular
Quality Content – finance, science, etc – not good candidates
No mechanism for improving folksonomies
Scale – Too Big (million hits) – Too Little (200 items) – Amazon and LibraryThing
Need intrinsic value of tagging – not tagging for better tags
Bad Tags - idiosyncratic or too broad, errors, limited reach
Most people can’t tag very well – learned skill

18. Del.icio.us Tags
Design blog software music tools reference art video programming webdesign web2.0 mac howto linux tutorial web free news photography shopping blogs css imported education travel javascript food games
Development inspiration politics flash apple tips java google osx business windows iphone science productivity books toread helath funny internet wordpress ajax ruby research humor fun technology search opensource
Photoshop media recipes cool work article marketing security mobile jobs rails lifehacks tutorials resources php social download diy ubuntu freeware portfolio photo movies writing graphics youtube audio online

19. Del.icio.us - Folksonomy Findability
Too many hits (where have we heard that before?)
Design – 1 Mil, software – 931,259, sex – 129,468
No plurals, stemming (singular preferred)
Folksonomy – 14,073, folksonomies – 3,843, both – 1,891
Blog-1.7M, blogs – 516,340, Weblog- 155,917, weblogs – 36,434, blogging – 157,922, bloging – 697
Taxonomy – 9.683, taxonomies – 1,574
Personal tags – cool, fun, funny, etc
Good for social research, not finding documents or sites
How good for personal use? Funny is time dependent

20. Library Thing Book people aren’t much better at tagging
High level concepts – psychology (55,000), religion (120,000), science (101,000)
Issue – variety of terms – cognitive science – need at least 40 other tags to cover the actual field of cognitive science
Strange tags – book (19,000) – it’s a book site?
Combination of facets and topics
Facets – Date (16th century, 1950’s, 2007) // Function (owned, not read) // Type (graphic novel, novel) // Genre (horror, mystery)
Topics – majority like Del.icio.us

21. Library Thing – Book on Neuroscience
1) (Location: dining room)(1) biological(1) biology(8) box74(1) Brain(1) brain research(1) brains(1) cognitive neuroscience(1) cognitive science(1) consciousness(1) currently reading(1) HelixHealth(1) kognitionswissenschaft(1) medical(1) medicine(1) neuroscience(19) non-fiction(5) partread(1) Psychology(4) Science(10) textbook(10) theory(1)
Too General: Science, Psychology, biology, textbook
Too specific: Location: dining room, box74
Facets: currently reading, partread

22. Better Folksonomies: Will social networking make tags better?
Not so far – example of Del.icio.us – same tags
Quality and Popularity are very different things
Most people don’t tag, don’t re-tag
Study – folksonomies follow NISO guidelines – nouns, etc – but do they actually work – see analysis
Most tags deal with computers and are created by people that love to do this stuff – not regular users and infrequent users – Beware true believers!

23. Browse Taxonomies: Strengths and Weaknesses
Strengths: Browse is better than search
Context and discovery
Browse by task, type, etc.
Weaknesses:
Mix of organization
Catalogs, alphabetical listings, inventories
Subject matter, functional, publisher, document type
Vocabulary and nomenclature Issues
Problems with maintenance, new material
Poor granularity and little relationship between parts.
Web site unit of organization
No foundation for standards

24. Formal Taxonomies: Strengths and Weaknesses
Fixed Resource – little or no maintenance
Communication Platform – share ideas, standards
Infrastructure Resource
Controlled vocabulary and keywords
More depth, finer granularity
Weaknesses:
Difficult to develop and customize
Don’t reflect users’ perspectives
Users have to adapt to language

25. Faceted Navigation: Strengths and Weaknesses
More intuitive – easy to guess what is behind each door
20 questions – we know and use
Dynamic selection of categories
Allow multiple perspectives
Trick Users into “using” Advanced Search
wine where color = red, price = x-y, etc..
Weaknesses:
Difficulty of expressing complex relationships
Simplicity of internal organization
Loss of Browse Context
Difficult to grasp scope and relationships
Limited Domain Applicability – type and size
Entities not concepts, documents, web sites

26. Dynamic Classification / Faceted navigation
Search and browse better than either alone
Categorized search – context
Browse as an advanced search
Dynamic search and browse is best
Can’t predict all the ways people think
Advanced cognitive differences
Panda, Monkey, Banana
Can’t predict all the questions and activities
Intersections of what users are looking for and what documents are often about
China and Biotech
Economics and Regulatory

27. Varieties of Taxonomy/ Text Analytics Software
Taxonomy Management
Text Analytics
Auto-Categorization, Entity Extraction
Sentiment Analysis
Software Platforms
Content Management, Search
Application Specific
Business Intelligence

28. Vendors of Taxonomy/ Text Analytics Software
Attensity
Business Objects – Inxight
Clarabridge
ClearForest
Data Harmony / Access Innovations
Lexalytics
Multi-Tes
Nstein
SchemaLogic
Teragram
Wikionomy
Wordmap
Lots More

29. Why Taxonomy Software? If you have to ask, you can’t afford it
Spreadsheets
Good for calculations, days of taxonomy development over
(almost)
Ease of use – more productive
Increase speed of taxonomy development
Better Quality – synonyms, related terms, etc.
Distributed development – lower cost, user input (good and bad)

30. Text Analytics Software – Features
Taxonomy Management Functions
Entity Extraction
Multiple types, custom classes
Auto-categorization – Taxonomy Structure
Training sets – Bayesian, Vector space
Terms – literal strings, stemming, dictionary of related terms
Rules – simple – position in text (Title, body, url)
Boolean– Full search syntax – AND, OR, NOT
Advanced – NEAR (#), PARAGRAPH, SENTENCE
Advanced Features
Facts / ontologies /Semantic Web – RDF +
Sentiment Analysis

31. Conclusion Variety of information and knowledge structures
Important to know what will solve what
Taxonomies and Facets are foundation elements
Build higher levels based on lower levels
Glossaries to Taxonomies
Taxonomy to Ontology / faceted navigation
Important to have good taxonomy and text analytics software (spreadsheets are OK for first draft)
Web 2.0/Folksonomies are not the answer

32. Resources Books Software Web Sites Women, Fire, and Dangerous Things
George Lakoff
Knowledge, Concepts, and Categories
Koen Lamberts and David Shanks
The Stuff of Thought – Steven Pinker
Software
Tools & Techniques (Taxonomy Boot Camp)
Web Sites
Taxonomy Community of Practice:

33. Questions? Tom Reamy tomr@kapsgroup.com KAPS Group
Knowledge Architecture Professional Services