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 evaluate suggestion 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 variation between teams

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

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_address]

The growing pains of a controlled vocabulary

by Karen Loasby on Apr 20, 2008

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