Taxonomy Fundamentals Workshop 2013

Taxonomy
Fundamentals
Workshop
Marjorie Hlava, President
Access Innovations, Inc.
www.accessinn.com
Taxonomy Boot Camp
Tuesday, November 5, 2013

© Access Innovations, Inc. All Rights Reserved.

Taxonomy Fundamentals Workshop
10:15 a.m. - 12:00 p.m.
Marjorie M.K. Hlava, President & Chairman, Access Innovations, Inc., creator of
Data Harmony software. My blog is TaxoDiary.com
This interactive session starts by building a solid conceptual foundation for
taxonomy creation and reinforces those concepts through audience participation.
Starting with the basics, Hlava quickly advances to where and how to leverage
taxonomies. This gives beginning and intermediate practitioners a good overview
of the foundational knowledge for the more advanced sessions throughout the
conference. Leveraging the taxonomy standards for the key components of a
thesaurus, Hlava explores how those elements support the information needs of
users from multiple perspectives and examines illustrative sites and behind-thescenes solutions to see how a well-constructed taxonomy with a rich interplay of
terms and synonyms leads to better information access. The workshop discusses
developing a taxonomy that serves users, respecting their needs for specialized
vocabularies. With hands-on activities, attendees gain insight into how a subject
area can be viewed, described, and structured. This learn-by-doing session
provides basic knowledge to create a taxonomy that suits your needs.


In our 1:45 hours together


Conceptual Framework - The Basics
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
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Where and How to Leverage Taxonomies
Better Information Access (Search)




“Card Sort”

“Taxonomatch”

A Quick Look at Standards


“A Taxing Situation” – “Taxopoly”


Conceptual Framework –
The Basics

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


What is a taxonomy?
What are the parts of a taxonomy?
How do you build one?
Guidelines for the terms
Subject matter experts (SMEs)


What is a Taxonomy?
ANSI/NISO Z39.19-2005
(R2010)
controlled

“A collection of controlled vocabulary terms
organized into a Yes!
hierarchical structure.”
Missing:
equivalence, associative relationships, and notes


The Semantic Roadmap:
Knowledge Organization Systems
•Linked Entities
•Contextual Specificity

•Complex
•High value










Semantic network
Ontology
Thesaurus
Taxonomy
Controlled vocabulary
Synonym set/ring
Name authority file
Uncontrolled list

•Simple
•Low Value

 Uncontrolled list
Highest Cost over
Time!


•Unrelated Entities
•Ambiguity

Basic features –
The term record
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Main Term (MT)
Top Term (TT)
Broader Terms (BT)
Narrower Terms (NT)
Related Terms (RT)


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ONTOLOGY
THESAURUS

Non-Preferred Term (NP)




See also (SA)

TAXONOMY

Used for (UF), See (S)
Synonyms

Scope Note (SN)
History (H)

= subject term, heading, node,
category, descriptor, class

Taxonomy? Thesaurus?



Often used interchangeably
Thesaurus is a taxonomy with extras


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


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Related Terms
Non-preferred Terms (USE/Used for)
Scope Notes
More

Taxonomies often have the actual
information object at the final node.
CMS and SharePoint tend to the
hierarchical view only, definition, and USE

Taxonomy
view

Copyright © 2005 © Access Innovations, Inc. All Rights Reserved.
Access Innovations, In

Thesaurus
Term Record
view

How do you build a taxonomy
?
•
•
•
•
•
•

Define subject field
Collect terms
Organize terms
Fill in gaps
Flesh out and interrelate terms
Apply to your data
You’re done!

Define subject field



Review representative collection of content
Determine:
 Core areas
Sociology


Peripheral topics

Psychology
Education

Law

 Scope can be modified later

Build, buy, augment?





Survey existing thesaurus/taxonomy resources for your
domain
Test for
•
Scope
•
Depth
•
Make-or-break terms
•
Cost
Adoption of existing taxonomies
 Term registries
 TaxoBank
 Taxonomy Warehouse
 Other resources
Don’t reinvent the wheel!

Foundations




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Start with what is known
Build from there
Use the literature, your data
Use the lists you already have internally
Build in continuous review throughout the
process, and beyond
Who is involved?






Taxonomists
Subject matter experts
Project management
Users

Collect terms

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Your documents and databases
Departmental terminology
Textbooks and their indexes
Book tables of contents and indexes
Journal quarterly indexes
Encyclopedias
Lexicons, glossaries on the topic
Web resources
Users and experts
Search logs

Gather terms from search logs



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Top ~100 search terms from search logs
Terms used more than 50 times
Match to web site with appropriate
answer
Basis for favorites or best bets, presented
at the top of results list
Behavior-based taxonomy


Extract the terms – N-grams




Mine the full text for terms
Decide term length






Up to four perhaps

Sort into a frequency list
Leave full strings and just n-grams
Auto match to other lists

Consolidate



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Search log terms
Source terms
N-gram extractions
De-dupe and frequency
Work the list


How do you choose terms?








Importance in the subject area
Use in the literature, by the organization
or community
Necessary degree of specificity or detail
Relationship with other controlled
vocabularies
Single concept = single term

One term / one concept






Terms represent simple or unitary concept
A unit of thought
May be a single-word term
May be a multi-word term if required to
“A unit of thought, formed by
represent the concept
mentally combining some or all
Three main categories
of the characteristics of a
–

–
–

Concrete entities
Abstract concepts
Proper nouns

concrete or abstract, real or
imaginary object. Concepts
exist in the mind as abstract
entities independent of terms
used to express them.”

How big should it be?



Depends on use and your content
What do the users need?






If search logs show precise detailed requests
Support them

Retail sites – less deep, more “facets”
Scholarly publishers – deep and specific


The levels
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



7 – 22 top terms
Cognitive width supports this range
3 levels for e-commerce
Roll up if you have more levels
Index / tag to the most specific level
More for specific and precise data
Smaller taxos are tougher to maintain
22 x 22 = 484 x 22 = 10,648

Sample vocabulary sizes
Scholarly
AIP
IOP
JSTOR
OSA
SPIE
NICEM

~6500 terms
~5400 terms
~57000 terms
~2400 terms
~3800 terms

Physics
Physics
General
Optics
Optical Physics
and Imaging
~5500 terms Education


Sample vocabulary sizes
Retail


Barnes & Noble




LL Bean


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16 TT 30 level 2 = 480

Home Depot


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10 TT 3 levels 10 / level = 1000 terms

Amazon




14TT 30 level 2 = 420 terms

14 TT – 3levels – 8 per level = 896 terms

For more information on product taxos http://gilbaneboston.com/12/presentations/T11_Hedden.pdf


Concrete entities as terms
•

Things and their physical parts
–

Birds
•

•

Buildings
•

•

Floors

Materials
–
–
–

–

Feathers

Cement
Wood
Lead

Cards and chips

Abstract concepts as terms
•

Actions and events
–

•

Abstract entities
–

•

strength, efficiency

Disciplines and sciences
–

•

law, theory

Properties of things, materials, and
actions
–

•

evolution, skating, management, ceremonies

physics, meteorology, mathematics

Units of measurement
–

pounds, kilograms, miles, meters, nanoseconds

Proper nouns as terms



Individual entities – “classes of one” –
expressed as proper nouns


San Francisco, Lake Michigan
Thesaurus standards exclude proper names,
persons, and trade names  authority files.
Taxonomies include them as final nodes.

Organize terms – roughly


Sort terms into several major categories –
logical groups of similar concepts as Top
Terms

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



Identify core areas and peripheral topics
10 – 20 to start
Consider moving proper names to authority files

Result: loose collection of terms under
several main headings


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Rough and tentative – see how it fits as you go
Initial gap analysis
Add / modify / delete as needed

How do terms relate?


Hierarchical relationships
-- Parents and their children





Equivalence relationships
-- Aliases
Associative relationships
-- Related terms
-- Cousins
-- See Also’s


TAXONOMY

THESAURUS

Hierarchical relationships



Broader Term represents the class,
whole, or genus
Narrower Term is a member, part, or
species

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
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Generic relationship
Whole-part relationship
Instance relationship

NTs inherit all the BT characteristics
BTs/NTs have a reciprocal relationship

Broader to Narrower Terms
Communications equipment
Telephones
Smartphones
Radio phones
Analog phones
Speaker phones


Hierarchy –
Whole-part relationship


Four general types
–

Body systems and organs
•

–

Geographical locations
•

–

Bernalillo County  Albuquerque

Fields of study
•

–

Ear  Middle ear

Geology  Physical geology

Hierarchical social structures
•

Ontario  Manitoulin District

Hierarchy –
Instance relationship


General category (common noun) as BT,
with individual example (proper noun) as
NTI (Narrower Term Instance)

Seas

French cathedrals

Baltic Sea
Caspian Sea
Mediterranean Sea

Chartres Cathedral
Rheims Cathedral
Rouen Cathedral

Essentially identical to “final node” in some taxonomies

Polyhierarchical relationship
•

•

Term can logically fit under more than one
Broader Term – can have Multiple Broader
Terms (MBT)
Part of ISO and ANSI/NISO standards
Nurses
Nurse administrators

Health administrators
Nurse administrators

Finance
Accounting

Careers
Accounting


Generic relationship test – 1
•
•

Both terms in same fundamental category
“All-and-some” test
Rodents
SOME

SOME

Squirrels
Pests
Squirrels

ALL

NOT ALL

Inheritance or inclusion – what’s true of the parent (BT)
is true for all children (NTs)

Generic relationship test – 2

Rodents
Squirrels

Pests

 ALL squirrels are rodents
x NOT ALL squirrels are pests
x NOT ALL pests are rodents

Equivalence relationship
•

Preferred Term
–
–

•

Thesaurus term and valid for indexing
Thesaurus notation: USE

Non-Preferred Term
–
–
–
–

Not valid for indexing
An alias
Entry point, directs user to Preferred Term
Thesaurus notation: UF or NPT

Spiders
UF Arachnids

Plant pathology
USE Phytopathology

Equivalence – when to use



Synonyms, slang, quasi-synonyms
Scientific and trade names




UF Motrin

Lexical variants

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

Ibuprofen
Fiber optics
Mouse

UF Fibre optics
UF Mice

Upward posting of narrow concepts not specified
in taxonomy or thesaurus


Social class

UF Elite, Middle class, Working class

Get equivalent terms from search logs, brainstorming…

Associative relationship


Related Terms (RTs) – cousins



“…terms related conceptually but not
hierarchically, and are not part of an equivalence
set” (i.e. not synonyms)



Both valid for indexing
Reciprocal relationship with each other
Expands user’s awareness, reflects
thesaurus coverage of unanticipated areas
Main basis for the ontology
14 main options offered in Z39.19

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Scope Notes (SN)
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Indicate meaning of the term in the context of
this thesaurus, for this audience
 Stress – Metal, Psychological, Physiological
Could be the definition or glossary
Indicate any restriction in meaning
Indicate range of topics covered
Provide direction for indexers; for terms often
confused, may suggest an alternative term
Use as needed – may not be for every term
Use a style guide
Be concise

Stating the terms
•
•
•

•
•
•

•
•

Term format
Grammatical issues
Singular and plural forms
Spelling
Abbreviations and acronyms
Capitalization
Other punctuation
Consistency

Term format


KISS – Keep it short and simple
•
•
•



Establish a policy
•



1-2-3 words
Effect on search
Pre- and post-coordination
E.g., follow Chicago Manual of Style

Grammatical issues
•
•
•
•
•

Nouns and noun phrases
Verbs  Gerunds
Adjectives - no
Adverbs - no
Initial articles – no

Compound Terms




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“Terms in a thesaurus should represent
simple or unitary concepts…” (ISO standard)
“Compound terms should be factored
(split) into simple elements…” (ANSI/NISO
standard)
Term phrases are okay (bigrams)

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

Adjective Noun
American history

Two concepts combined are not


Aromatherapy for bloating

Pre- and Post-Coordination


Pre-coordination – multiple concepts


Subject headings – Library of Congress


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

American history – Civil War

Back of the book
Put together in advance by the publisher

Post-coordination

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

Taxonomy terms
Single concept
Put together by the user / searcher

So far you’ve got


Hierarchy
–
•

–

Preferred/Non-Preferred Terms
–

–

–

Equivalence relationships

Related Terms
–

–

Broader and Narrower Terms
Polyhierarchies when needed

Associative relationships

Scope Notes
Complete term records
–

Correct term format

Taxonomy
view


Thesaurus
Term Record
view

Does it work?


Test on your data

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

Index 500+ documents (more for variable writing style; fewer
for strict style)
No un-indexed articles allowed
Consider deleting unused terms

Review



Users
Expert reviewers

Consider automated / assisted indexing software

Subject Matter Experts
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Work first from the literature
Establish literary warrant for terms
Someone else do the clerical work
Differentiate the lexicography work



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From the subject matter expert work

Let SMEs do the review and tailoring
Expert review ensures the proper term
use and application

Subject Matter Experts


Interview




Make suggested enhancements





1 – 2 hours
8 hours

Balance competing perspectives
Advisory Board…advisable!


Review, edit, test, edit,
use, edit, and maintain, i.e. edit

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Monitor search logs
Allow indexers to suggest candidate terms
Edit and maintain
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Add term
Change existing term
Change term status
Delete term
Add term relationship
Delete term relationship
Add/modify scope note
Change overall structure

Card Sort
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Groups of three
Organize terms into the “proper”
hierarchical order
Use as many levels as needed
Use polyhierarchy as needed
Write your top terms on the flip chart
sheet to show the group
15 minutes

Where and how to leverage
taxonomies

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
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Implementation and applications
Adding the terms to the information
objects
Search and other applications
Taxonomy use cases – implementation
Opportunities and obstacles


Parts of the puzzle


The taxonomy






Applications




Search, Web site, CMS, SharePoint, Publishing system,
Author submission, Peer reviewer ID, Recommendation
engines, etc.

Implementation / actions

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



The words to use
In the order you want the users to browse

Making the links
Adding terms to information objects
Mash-ups

Most people confuse the parts, but they act very
differently

Fully integrated with MOSS
The Workflow
Gather
source
data
Client Data
Full Text

Tag and
Create
metadata

Put in
data base
with tags

Build
Search
inverted
index

Automatic
Summarization

Search
Presentation
Layer

HTML, PDF,
Data Feeds,
etc.

Machine Aided
Indexer
(M.A.I.™)

Inline Tagging

Client
Client Taxonomy
taxonomy

Create
user
interface

Database
Repository

Metadata and
Entity Extractor
Thesaurus
Master


Search
Software

Increases
accuracy
Browse by Subject
Auto-completion
Broader Terms
Narrower Terms
Related Terms

Adding terms to
information objects


Part of the record




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A relational table pointing the terms to a
record ID number (Secondary key)
Adding data to the HTML




XML
MARC

META NAME KEYWORD Element

Many other options


Part of the record –
XML



Added as an element in the XML record
Need an element to put the data in





Element = Field = table value = <Taxonomy
Term>
<Taxonomy Term>Roadworks</Taxonomy
Term>

Capture the terms when creating the
records

Part of the record MARC



Added as an element in the MARC record
Need an element to put the data in







654 Roadworks
345 Roadworks

Wherever you decide it works best for
your OPAC
Add the terms when creating the record


Editorial Workflow Integration
Author Submission Module
The author fills in the data to the document template,
attaching images and graphs as necessary.
An API calls Data Harmony and generates a list of indexing
terms based on the content.


Author Submission Module
Authors review the
indexing and may
change it.

Content is stored
into a data
repository as
HTML, XML, etc.


Contributor Role Tagging
A popup list of contributor role options appears for the author to
choose from
----Study conception ?

Contributor Information

Contributor Role

--Methodology ?
Mouse Over
Formal analysis ?
for explanation
Computation ?
Investigation ?
Application of
Resources ?
statistical,
Data Curation ?
mathematical, or
Publication ?
other formal
---Supervision ?
techniques to
--Project administration ?
analyze study data
Funding acquisition ?

Formal Analysis

In the HTML record



Makes it crawlable for the internet
Used in CMS applications




Add to the HTML






Content management systems
Manually
In Dreamweaver
In your CMS system (Drupal, WordPress, etc.)

Author Submissions Example

META NAME “KEYWORDS”


In Relational Database
Table



Primary key – for the record
Secondary key all the metadata






Used in Oracle, SQL, etc.




Like taxonomy terms
Like author
Like publication date
Need field to put the taxonomy data in

Supports “Faceted Search”


each item in a separate field or element or table

Relational database diagram


User uploads a document
to SharePoint space

Data Harmony
automatically attaches
indexing terms before
uploading to MOSS

Before uploading to
SharePoint server, the
EventHandler sends the
document to Data
Harmony.

TaxoTerm Server
Data Harmony
(M.A.I.)

Adding terms
to SharePoint

Returns subject
metadata


Microsoft
SharePoint
Server 2010

SharePoint 2010 shows only
10 lines of the taxonomy
This add-on makes it all viewable


Taxonomies added in
search example
Core Architectural Components
Administrator’s
Dashboard

FAST MANAGEMENT API

EMAIL
CONNECTOR

Email,
Groupware
Content
Push

FILTER
SERVER

Alerts

CUSTOM
CONNECTOR
MAIstro

Agent DB

Use taxonomy terms here
Data Harmony Governance API

Query

Vertical
Applications
Portals

Results

Search harmony

Custom
Applications

Index DB

Pipeline

QUERY
PROCESSOR

Databases

DATABASE
CONNECTOR

Pipeline

DOCUMENT
PROCESSOR

FILE
TRAVERSER

CONTENT API

Files,
Documents

SEARCH
SERVER

QUERY API

WEB
CRAWLER

Web
Content

Custom
Front-Ends
Mobile
Devices

Autosuggestion of
taxonomy terms
Populate
keywords,
descriptors,
indexing terms,
etc.

Allow for manual
review of autotagging for
quality
assurance.

HTML Header


Suggested taxonomy descriptors

Dynamic Display

71

Inline Tagging

Shows the exact point where the
concept is mentioned
Mouse-over to view the term record


Statistical summary, showing the
number of times each term is
mentioned in the article

Integrate taxonomy
to enhance findability


Browsable categories of a directory






Browsable faceted navigation

Smart search for term equivalents
Taxonomy terms (original or modified) as labels
Navigation aids incorporate taxonomy terms
and relationships


More Taxonomy
Enrichment







Spelling alternatives and correction
Related concepts
Statistical information about the metadata
Navigation or drill-downs
Search refinement






Recursive sets

Concept linking
Dictionary lookup (in taxonomy glossary)


Parts of Search


Search software





Inverted index
Search algorithms

Presentation layer





Search box
Auto-completion
Related and narrower terms
Hierarchical display

Database Plus Search Workflow
SQL for
ecommerce

Raw Full
text data
feeds
Printed
source
materials
Data
Crawls on
data
sources

Source data

XIS
Creation
XIS
repository

Load to
Search

Taxonomy
terms
MAI Concept
Extractor
MAI Rule
Base
Taxonomy
Thesaurus
Master
Add
metadata

Clean and enhance data

Search
Harmony
Display
Search

Search data

Why does search fail?


Most large organizations have 5 different
search software applications







All disappointing and on the shelf

Inconsistent results
Unclear path to results
Lack of single unified clear and consistent
vocabulary
Not tied to data governance



Taxonomy
Other metadata

Sample DOCUMENT
Outline of Presentation
1

Creating
an
Inverted
File Index

2

Define key terminology
Thesaurus tools



3

Costs



4

Features
Functions
Thesaurus construction
Thesaurus tools

Why & when?


Simple inverted file index
The terms from the “outline”
&
1
2
3
4
construction
costs
define
features
functions


key
of
outline
presentation
terminology
thesaurus
tools
when
why

Complex inverted file index
Placement location
& - Stop
1 - Stop
2 - Stop
3 - Stop
4 - Stop
construction - L7, P2, SH
costs - L6, P1, H
define - L2, P1, H
features - L4, P1, SH
functions - L5, P1, SH

key - L2, P2, H
of - Stop
outline - L1, P1, T
presentation - L1, P3, T
terminology - L2, P3, H
thesaurus - (1) - L3, P1, H
(2) - L7, P1, SH
(3) - L8, P1, SH
tools - (1) - L3, P2, H
(2) - L8, P2, SH
when - L9, P3, H
why - L9, P1, H


Access Innovations –
Complex Farm
with Perfect Search

Query

Query Servers
Search Harmony
Presentation
Layer

Federators

Cleanup, etc.

Deploy
Hub

Repository XIS
(cache)

Cache
Builders
Source
Data

Index
Builders

Measuring accuracy in search














Relevance
Recall
Precision
Hits, misses, noise
Ranking
Linguistics
Query processing
Results processing
Display
Search refinement
Usability
Business rules

Relevance





How well a set of returned documents answers
the information need
“Accuracy”
Related to objective of search






Different user communities
Information resources

Tension of user needs and context available
A confidence “guessimate”


The formulas
Recall = Number of relevant items retrieved
Number of relevant items in the collection
Precision = Number of relevant items retrieved
Number of items retrieved
Relevance = Germane (Precision)
Pertinent (Recall)


Measuring Relevance










Concepts
Context
Age of documents
Completeness (recall)
Quality
Statistically determined?
Nope, it is subjective




Someone has to determine the rightness of the item

A confidence factor = canard!

Improve Search
www.mediasleuth.com

Auto-completion using the
taxonomy

Guide the user

Navigate
the full
taxonomy
“tree”
BROWSE


Subject Browsing


Targeted resources based
on subject or user role

CONFIDENTIAL

Link to Society Resources
Cancer Epidemiology Biomarkers & Prevention
Vol. 12, 161-164,
February 2003
© 2003 American Association for Cancer Research
Short Communications
Alcohol, Folate, Methionine, and Risk of Incident Breast
Cancer in the American Cancer Society Cancer Prevention
Study II Nutrition Cohort
Heather Spencer Feigelson1, Carolyn R. Jonas, Andreas S.
Robertson, Marjorie L. McCullough, Michael J. Thun and
Eugenia E. Calle Department of Epidemiology and Surveillance
Research, American Cancer Society, National Home Office,
Atlanta, Georgia 30329-4251
Recent studies suggest that the increased risk of breast cancer
associated with alcohol consumption may be reduced by
adequate folate intake. We examined this question among
66,561 postmenopausal women in the American Cancer Society
Related Working Groups
Think Tank Report
•FinancePrevention Study II NutritionRelated Think Tank Report
Cancer
Cohort.
•Charter
Content
•Molecular Epidemiology
Webcasts
Related Awards
Related Webcasts
•AACR-GlaxoSmithKline Clinical Cancer Research
Scholar Awards
•ACS Award
•Weinstein Distinguished Lecture


Related Press Releases
•How What and How Much We Eat (And Drink) Affects Our
Risk of Cancer
•Novel COX-2 Combination Treatment May Reduce Colon
Cancer Risk Combination Regimen of COX-2 Inhibitor and
Fish Oil Causes Cell Death
•COX-2 Levels Are Elevated in Smokers

Related AACR Workshops and Conferences
•Frontiers in Cancer Prevention Research
•Continuing Medical Education (CME)
•Molecular Targets and Cancer Therapeutics
Related Meeting Abstracts
•Association between dietary folate intake, alcohol intake, and
methylenetetrahydrofolate reductase C677T and A1298C
polymorphisms and subsequent breast
•Folate, folate cofactor, and alcohol intakes and risk for
colorectal adenoma
•Dietary folate intake and risk of prostate cancer in a large
prospective cohort study

Related Education Book Content
Oral Contraceptives, Postmenopausal Hormones,
and Breast Cancer
Physical Activity and Cancer
Hormonal Interventions: From Adjuvant Therapy to
Breast Cancer Prevention

After Helen Atkins

Linked Data
Other Journal
Articles on
Topic A

CME Activity
on Topic A

Journal
Article on
Topic A

Grant Available for
Researchers
Working on Topic A

Upcoming
Conference
on Topic A

Job Posting
for Expert
on Topic A

Podcast Interview
with Researcher
Working on Topic A
Author Networks
Social Networking


After Helen Atkins

Authors at a Place


Member Profile Tagging

User pastes or
uploads CV

Button to autoextract taxonomy
attributes






Designed to enhance understanding and retention of the
vocabulary concepts necessary for creating a taxonomy,
ontology, thesaurus, or controlled vocabulary.
Game supplies:





Game setup:








1 Deck of Orange Question and Challenge Cards
1 Deck of Green Answer Cards
Shuffle the deck of Green Answer cards.
Deal the entire deck to the players.
Shuffle the deck of Orange Question and Challenge cards.
Place them face down in a pile in the middle of the table so that all
players can reach the pile.

Reinforce what you just heard!
Have fun!

1.
2.

3.

4.

5.

6.

Play moves to the left of the dealer.
7.
Draw a card from the top of the Orange
cards. Read it aloud to all of the players.
The player who read the card says out loud 8.
what they think the answer is.
Each player looks at the Green Answer
cards in their hand.
1.
If they have the correct answer to the
Question or Challenge, they show their
9.
card to everyone at the table.
2.
If everyone agrees that the answer is
correct, the player holding the correct
answer card gives it to the player who
10.
read the Question or Challenge card.
The player places their associated pair of
cards – one Orange Question and
Challenge card and one Green Answer card
– face up on the table in front of them.
Play passes to the person who held the
correct Green Answer card in their hand.
Play continues as in step 2 above.

Discussion among the players to arrive at
the correct answer is permissible and
encouraged!
If players do not arrive at a consensus
regarding the correct answer, the Orange
Question and Challenge card may be
returned to the bottom of the pile, and play
passes to the person to the left of the player
who drew the previous card.
When all of the Orange Question and
Challenge cards have been drawn, read
aloud, and matched with their Green
Answer cards, the game ends.
If there are any Orange Question and
Challenge cards remaining to which players
cannot agree on an answer, players may
consult their notes or ask the session
speaker.

Using taxonomies in
applications
•
•
•
•
•
•
•
•

•
•
•

Improve search
•
Subject browsing
Mobile intelligence
Targeted resources based •
on subject or user role
Link to society resources •
Author submission module •
Author authority database •
•
Expert reviewer
•
identification
Member profiles
•
Data visualization

More like this

In “indexing” or
categorizing, as subject
metadata
In content management
systems
In SharePoint
In mash-ups
In social networking sites
In author tagging
In filtering data – e.g.,
spam filters and RSS feeds
In web crawlers
Social media - community

More Innovations!










Link topic to article to author to event
Make visual links within domain
Enable authors to submit and categorize conference
submissions
Create author authority database linking to co-authors,
topics, locations, etc.
Create expert reviewer database
Create member profiles with alternate names,
publications, tagged by topic
Visualize data and domain distribution
Display interest connections in social network
Deliver accurate targeted information through mobile
applications

Visualize your tagged data

This is a radial
graph of
“plosthes”. The
number of
records for which
each index term
occurs is
reflected by
circle sizes.


All data up-posted to the
top level


Load to a visualization program
such as Prefuse


Thesaurus, Controlled vocabulary, ontology...
Taxonomy only as part of thesaurus


Taxonomy standards









Z39.19 (2005; reaffirmed 2010) Controlled
Vocabularies
BS 8723 Parts 1 – 5
ISO 25964 Parts 1 and 2
TAG 37 and 46 standards
SKOS - Simple Knowledge Organization
System
OWL - Web Ontology Language
AND more!

Taxonomies don’t exist in a
vacuum




They are part of metadata
They are used to tag information objects
They are used







On Web sites
In search
To profile people
To link resources

So we have to know a little about those
standards as well

More on ISO 25964





Part 2 Interoperability
and RDA
at 2:15 PM


W3C





HTML 5
Linked Data
Ontologies (OWL) and SKOS




Cascading Style Sheets (CSS)




Simple Knowledge Organization System

Adding style to Web content

Widgets



Widget Packaging and XML Configuration,
Widget Interface




API to metadata and persistently storing data

XML Digital Signatures for Widgets

Big Library Followings


DCMI – Dublin Core Metadata Initiative




Functional requirements

Library of Congress


109


Library of Congress










MARC 21 formats and MARCXML
VRA Core -- them
METS (Metadata Encoding & Transmission
Standard)
MIX (NISO Metadata for Images in XML)
PREMIS (Preservation Metadata)
TextMD (Technical Metadata for Text)
ALTO - Technical Metadata for Optical
Character Recognition
Extended Date/Time Format (EDTF)

Thesaurus related












NISO Z39.19 2010 www.niso.org
ISO 2788 - Monolingual (1986) (withdrawn)
ISO 5964 - Multilingual (1985) (withdrawn)
ISO 5127, Information and documentation 
Vocabulary
BS 8723 (withdrawn) (basis for revised ISO)
ISO 25964 / Part 1 – Controlled Vocabularies
ISO 25964 / Part 2 – Taxonomy Interoperability
Dublin Core DCMI Functional requirements
SKOS – the W3C thesaurus standard
OWL from W3C

Thesaurus and Indexing
Standards – ANSI/NISO


NISO Z39.19-2005 (R2010) Guidelines for the
Construction, Format, and Management of
Monolingual Controlled Vocabularies



NISO TR02-1997 Guidelines for Indexes and
Related Information Retrieval Devices
by James D. Anderson


New ISO Taxonomy
Standard



ISO 25964. Thesauri and interoperability
with other vocabularies




Part 1: Thesauri for information retrieval
Part 2: Interoperability with other vocabularies
Stella Dextre Clarke, principal author


W3C



OWL – Web Ontology Language
RDF – Resource Description Format
Topic Maps
SKOS - Simple Knowledge Organization
System
SKOS 2 DCMI
TURTLE



Which community to serve?








Other Relevant ISO &
W3C Standards


Metadata standards overview




http://www.slis.kent.edu/~mzeng/metadatabasics/
completelist.htm

Review of SKOS / DCMI / Taxonomy
Standards


http://nkos.slis.kent.edu/


SKOS


SKOS 1 –





SKOS 2 –






no synonyms,
no polyhierarchies
Added the above
Allows other fields (elements) on request

OWL Crosswalk
NISO Z39.19, BSI 8723, and ISO 25964

Who supports SKOS Everyone










Data Harmony Thesaurus Master
Synaptica
SmartLogic
WordMap
PoolParty
Top Quadrant
Protégé
Etc.

Standards and
pragmatism






Use Standards
 Lead to richer, more informative product
 Promote interoperability -- Allow you to adopt or
adapt other controlled vocabularies
 Promote predictability
 Allow repurposing within your organization and by
other organizations
Follow thesaurus standards for taxonomy
 Incorporate authority files / final nodes as needed
Your taxonomy or thesaurus must meet your needs

The Problem –
KEEPING UP






Many players we know and don’t know
Controlled vocabulary standards
Groups developing guidelines and standards
 W3C with SKOS and OWL
 Governments worldwide developing and
mandating taxonomies
Communities
 Increase reuse
 Mapping interoperability between controlled
vocabularies


Places to watch



Other W3C and ISO areas
Support groups









Blogs
Communities of Practice

WSDL – Web Services Digital Library
DCMI
NKOS
ISKO
Linked Data






The New Board Game
Applications
Implementation
The taxonomy


Where do I learn more?





Online resources
Taxonomy books
Those standards
Organizations




SLA Taxonomy Division
ISKO
NKOS


129


Lists of Taxonomy
Resources










Registry? NKOS KOS of KOS
SKOS participants – W3C
KOS typology – Tudhope
TaxoBank.org
Kent.edu site – Marcia Zeng
Taxonomy Warehouse – Synaptica
UMLS - Unified Medical Language
System - NIH

IT is often Fire, Ready, Aim!









Choose the hardware
Choose the software
Decide on the format
Convert the data
Fix the data
Tack on a taxonomy
Ignore the standards

Change to Ready, Aim, Fire!










Follow the data
Look at the data, format, and content
Design taxonomy for data
Leverage the standards
Use taxonomy to tag data
Choose search and repository software for
data
Load the data into the system
Keep your eye on the target

For copies of the
“The Games”


Summary










We covered the basics
We talked about the implementation
Application of the terms to your content
Search
Standards
We reinforced the learning with activities
You drank from the fire hose
Now go hear the case studies of the next
two days!

Marjorie M.K. Hlava

Thank you
for your
attention!

President
Access Innovations, Inc.
Data Harmony
mhlava@accessinn.com
505-998-0800
www.taxodiary.com - the
taxonomy news blog
mmkhlava = Twitter
mhlava = Facebook,
LinkedIn, eAcademy, Plaxo

Taxonomy Fundamentals Workshop 2013

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Editor's Notes