the growing pains of a controlled vocabulary

1Karen Loasby 7 March 2005

The growing pains of a controlled vocabulary


Introduction

• Karen Loasby• Information architect• Worked for BBC for 4 years on search,

navigation, metadata and content management projects

• 2 years previously for the Guardian newspaper archiving the paper and arranging content on the website

• MSc in Information Science from City University, London


Agenda

• Background

• The problem

• Formal classification vs. Folk tags

• Our middle ground

• What happened

• Learning points

• Questions


Background

• Content management project

• Regional websites

• Need for metadata

• Authors around the UK


Problem

• Faceted classification system

• Authors to tag

• Central control

• But …

• Journalists are the specialists – know the domain and the vocabulary.


Formal classification

• Pre-determined terms

• Centralised control• Rich relationships


Folk tags

• What it is then?• Folksonomy,

ethnoclassification, social classification, social categorisation and so on


Comparing approaches

Formal• High maintenance• Consistent/predictable• Rich relationships• Can be artificial

Folk• Low maintenance• Quirky/surprising• Less added value• Real user language


A role for both

• Where we are using folk tagging

• And where we won’t– Trust & Authority– High value to business– Missing motivation from users– Broad domain/user base– To avoid tryanny of minority


An experimental middle ground

• Centralised control of terms

• But encouraging absorption of user language

• Higher maintenance than folk tags

• Cheaper than professional cataloguing


BBC Experience

Semi-automatic classification

Terms suggested from the CVs

Terms are OK

The suggested terms do not describe

the content

Search or browse for terms

Send suggestion to the CV team

Terms are OK

Send suggestion to the CV team

CV team evaluatesuggestion Say no to the term

– change the classification on

the content object

Add to CV as a variant term

or preferred term


Operational system

• 8000 requests in 10 months

• From 160 journalists– Average per user of 50 terms– However this varied wildly. Our top user has

suggested 476 terms


Graph showing variationbetween teams

0

100

200

300

400

500

600

700

800cum

bria

tyne

cam

bridgeshire

guern

sey

leic

este

r

south

york

shire

wilt

shire

suffolk

liverp

ool

mancheste

r

berk

shire

bristo

l

kent

coventr

y

tees

jers

ey

sto

ke &

sta

ffs

nottin

gham

derb

y

hum

ber

som

ers

et

nort

ham

pto

nshire

norf

olk

beds, bucks &

hert

s

leeds

here

ford

& w

orc

s

birm

ingham


Growth in the CVs

• Up 15000 terms in 10 months

• Most growth in person/proper names • People, venues and organisations• Up by 50% to 35,000


Growth of facets

CV Requests By Month

0

1000

2000

3000

4000

5000

6000

7000

Month

Qu

an

tity Name

Location

Subject

BBC Brand

Time Period


Types of terms

• Mostly good– Only 200 terms actually rejected

• Synonyms vs. entirely new terms– New for names (only 2% synonyms)– Synonyms for subject (15% synonyms)– Location – needed colloquial terms


Resourcing

• Handling the requests from journalists

• First 3 months – one IA

• Subsequently 2 to 3 junior IAs

• Too much – how to reduce?


Lessons learned

• Success with the journalists– They suggested terms!– Got the faceted classification – Began to suggest terms in “our” format – Some did engage at a detailed level


Lessons Learnt

• Difficulties for journalists– System looks as if totally automatic as part of

a content management system– “Journalists are people too”

– Users struggling with a content object tagging system; rather than page based


Example

Subject: Pregnancy


Lessons Learnt• Difficulties for journalists, cont.

– They find it boring – Makes it harder for the aim of “finding and re-

use” to apply – Needed to do more pre-emptive work for them


Lessons learnt

• Number of terms suggested depends on– Type of facet– Dynamism of content– Scope of the content– Enthusiasm of users


Next?

• High value facets still need control– Make use of the metadata(!)– Sell the message– Federated management– Earlier in production

• And for folk tagging?


Thanks to the IA team for their analysis work;– Jon Carey– Adil Hussein– Christine Rimmer


Thank you

Questions or comments?

Karen Loasby

[email protected]

the growing pains of a controlled vocabulary

Technology

lessons learnt

folk tags

folk tagging

suggested

terms

journalists

metadata

subject