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

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

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

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.

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

Pre-determined terms

Centralised control

Rich relationships

Folk tags What it is then? Folksonomy, ethnoclassification, social classification, social categorisation and so on

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

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

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

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

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

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

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?

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

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

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

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

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?

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

Thanks to the IA team for their analysis work;

Jon Carey

Adil Hussein

Christine Rimmer

Thank you Questions or comments? Karen Loasby [email_address]