1. THAT'S NOT WHAT I MEANT!
identifying, clarifying and brokering consensus over taxonomy terms and keywords
Fran Alexander, Taxonomy Manager, Information and Archives, BBC
@frangle
http://www.vocabcontrol.com
*All views expressed here are entirely my own personal views and in no way represent the BBC or official BBC policy.

2. BBC Archive Centre
2 million items of TV and video
300,000 hours of audio
6 million still photographs
4 million pieces of sheet music
500,000 documents
4,000 loans per week

3. Overview
divided by a common language – the problem with words
us and them – categories and communities
semantic politics – brokering consensus
classification migration project
Sharepoint project
ontology project
top tips

4. Apple, Orange, Blackberry, Next!
ORANGE
4

5. He's a real dog

6. What are the odds?
• trumpet
• violin
• French horn
• trombone

7. • London
• China
• Brazil
• France

8. • French
• Spanish
• Hebrew
• Italian

9. • record
• object
• entity
• archive

10. • archive
• backup
• record
• library

11. • spook
• entity
• ghoul
• fairy

12. Top-down or bottom-up?
traditional classifications were made by
subdivision of pre-set classes
modern taxonomies tend to work by clustering or
grouping
more flexible systems, more closely related to
reality, but need to understand
users, viewpoints, contexts

14. Content engineers
Content Metadata Parametadata – “Meta-metadata”
(tag) Creator Taxonomy Date Approved
Canis lupus Researcher Telclass (specialist 4/4/11 JP
taxonomy)
Wolves Production BBC free tag 3/3/11 -
assistant
Grey wolf Cataloguer Lonclass (archival 14/4/11 JP
sleeping taxonomy)
/archives /archives team Special collections tag 12/1/11 JR
Wildlife
programme
Canidae Natural NHM taxonomy 11/6/11 CC
History
Museum
Wolf-spotting Member of Free tag/folksonomy 12/4/11 _
on holiday public
with Bob

15. Japan ese hon
“th e J a p a n e s e c la s s ifie r „H o n ‟ c la s s ifie s lo n g th in
o b je c ts ; s tic k s c a n e s , p e n c ils , c a n d le s , tre e s , ro p e s ,
h a ir, e tc . it c a n a ls o b e u s e d to c la s s ify d e a d s n a k e s
a n d d rie d fis h , w ith a re lo n g a n d th in . B u t it a ls o
in c lu d e s :
m a rtia l a rts c o n te s ts w ith s ta ffs o r s w o rd s
h its in b a s e b a ll
s e r v e s in v o lle yb a ll a n d ra llie s in p in g p o n g
ju d o m a tc h e s
l
ro le s o f ta p e
te le p h o n e c a lls (w h ic h c o m e o v e r lo n g th in
w ire s )
ra d io a n d T V p ro g ra m s (lik e p h o n e c a lls , b u t
w ith o u t th e w ire s )
le tte rs (s c ro lls a re th in )
film s (b e c a u s e th e y’re lik e ta p e )
in je c tio n s

16. D yirb a l c la s s ific a tio n
B a yi: m e n , k a n g a ro o s, p o ssu m s , b a ts, m o st
sn a ke s , m o st fish , so m e b ird s, m o s t in s e cts, th e
m o o n , sto rm s, ra in b o w s, b o o m e ra n g s
B a la n : w o m e n , b a n d ic o o ts , d o g s , p la typ u s,
e ch id n a , so m e sn a ke s, s o m e fish , m o s t b ird s,
fireflie s, sco rp io n s, cricke ts, th e h a iry m a ry g ru b ,
a n yth in g co n n e cte d w ith w a te r o r fire , su n a n d
sta rs
B a la m : a ll e d ib le fru it a n d th e p la n ts th a t b e a r
th e m , fe rn s, h o n e y, cig a re tte s, w in e , c a ke
B a la : p a rts o f th e b o d y, m e a t, b e e s, w in d ,
ya m sticks, so m e s p e a rs, m o st tre e s , g ra ss, m u d ,
sto n e s, n o ise s , la n g u a g e .

17. B o rg e s C e le stia l e m p o riu m o f b e n e vo le n t kn o w le d g e
“… o n th o se p a g e s it is w ritte n th a t a n im a ls a re
d ivid e d in to (a ) th o se th a t b e lo n g to th e e m p e ro r, (b )
e m b a lm e d o n e s, (c) th o se th a t a re tra in e d , (d )
su ck lin g p ig s, (e ) m e rm a id s, (f) fa b u lo u s o n e , (g ) stra y
d o g s, (h ) th o se th a t a re in clu d e d in th is cla ssifica tio n ,
(i) th o se th a t tre m b le a s if th e y w e re m a d , (j)
in n u m e ra b le o n e s, (k ) th o se d ra w n w ith a ve ry fin e
ca m e l‟s h a ir b ru sh , (l) o th e rs, (m ) th o se th a t h a ve ju st
b ro k e n a flo w e r va se , (n ) th o se th a t re se m b le flie s
from a d ista n ce .”

18. Sorted
meanings of words and labels –
how to make sure these are clear
users' language communities, basic
categories, contexts – how to
understand their viewpoints
practical methods to help you make
decisions

19. 19

20. How enterprising

21. Everybody counts
How do you run a card sort with a million terms?
How do you user test with 20,000 users?

22. Taking samples
selected representatives from different communities
ran a workshop on high level categories
top-down and bottom-up - mixed approach
sections assigned to editor/s and SMEs
all-editors regular discussion sessions
user feedback and iterative changes

23. What's your point?
navigation?
toolkit?
mandatory or suggested?
complete or selected?

24. Nice figure
command-and-control?
help and support?
existing structures
existing workflows
and
processes

25. Top models

26. Class act
think about
scope, purpose, users
simplification – what can
be ignored
shared understanding
may be more than just
getting labels right
http://www.bbc.co.uk/
ontologies/programmes/
2009-09-07.shtml

27. My domain is your kingdom
what is the same as something else?
what is Paris? an area, a city, a location in a film, an administrative district
does London include Stansted, Gatwick, and Luton?
what happens if we get this wrong?

28. Notice
understand how fuzzy language can be
think precisely and clearly
learn to spot “danger” words
become “conversational negotiators”
don’t underestimate how long you should
spend checking definitions with users
don’t underestimate importance of iteration
– even ripping up and starting again
more people you involve, better able to get a
clear view of an area
know when to stop and just decide

29. Find fault
label errors
puns, jokes, word games – to identify slippery words
arguments and points of failure – indicate lack of
shared understanding
search logs, analytics, questionnaires
be consistent in your own use of language
category errors
miscellaneous – change categories?
frequent category errors by users - change categories?
domain errors
never be afraid to question shared understanding
if you and your team don’t know how it is supposed to
work then no-one else will!

30. Refs
Alexander, F. (2012) Building bridges: Linking diverse classification schemes as
part of a technology change project, Journal of Business Information Review, vol.
29 no. 2, pp. 87-94. http://bir.sagepub.com/content/29/2/87.abstract
Alexander, F. (2012) Assessing information taxonomies using epistemology and
the sociology of science, Journal of Documentation, Vol. 68, Issue 5. DOI: 10.1108/
00220411211256058 http://www.emeraldinsight.com/journals.htm?issn=0022-
0418&volume=68&issue=5&articleid=17036853&show=pdf
Alexander, F. (2009) Trying to please everyone: The taxonomist as politician.
http://bit.ly/gC7MiW
Borges, J. L. (1942) The Analytical Language of John Wilkins (El idioma analítico de
John Wilkins).
Bowker, G. and Star, S. L. (1999). Sorting Things Out: Classification and Its
Consequences.
Brown, J. S. and Duguid, P. (2000). The Social Life of Information.
Lakoff, G. (1987). Women, Fire and Dangerous Things.
Lambe, P. (2007). Organising Knowledge: Taxonomies. Knowledge and
Organisational Effectiveness.
Olson, H. (2002). The Power to Name.
Wenger, E. (1999). Communities of Practice: Learning, Meaning, and Identity.

31. In credit
Labels:
http://www.flickr.com/photos/mrsmagic/5870198525/
Moo cards:
http://www.flickr.com/photos/philgyford/247592709/sizes/l/in/photostream/
Concert Crowd (Osheaga 2009):
http://www.flickr.com/photos/anirudhkoul/3786725982/sizes/l/in/photostream/