The growing pains of a controlled vocabulary

Slide 1: The growing pains of a controlled vocabulary 1 Karen Loasby 7 March 2005

Slide 2: 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 2 Karen Loasby 7 March 2005

Slide 3: Agenda Background • The problem • • Formal classification vs. Folk tags • Our middle ground • What happened Learning points • Questions • 3 Karen Loasby 7 March 2005

Slide 4: Background • Content management project • Regional websites • Need for metadata • Authors around the UK 4 Karen Loasby 7 March 2005

Slide 5: 5 Karen Loasby 7 March 2005

Slide 6: Problem Faceted classification system • Authors to tag • • Central control • But … • Journalists are the specialists – know the domain and the vocabulary. 6 Karen Loasby 7 March 2005

Slide 7: Formal classification • Pre-determined terms • Centralised control • Rich relationships 7 Karen Loasby 7 March 2005

Slide 8: Folk tags • What it is then? • Folksonomy, ethnoclassification, social classification, social categorisation and so on 8 Karen Loasby 7 March 2005

Slide 9: Comparing approaches Formal Folk • High maintenance • Low maintenance • Consistent/predictable • Quirky/surprising • Rich relationships • Less added value • Can be artificial • Real user language 9 Karen Loasby 7 March 2005

Slide 10: 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 10 Karen Loasby 7 March 2005

Slide 11: An experimental middle ground • Centralised control of terms • But encouraging absorption of user language • Higher maintenance than folk tags • Cheaper than professional cataloguing 11 Karen Loasby 7 March 2005

Slide 12: BBC Experience Terms are OK Terms are OK Search or browse Semi-automatic Terms suggested for terms classification from the CVs The suggested terms Send suggestion do not describe to the CV team the content Send suggestion to the CV team Add to CV as a variant term or preferred term CV team evaluate suggestion Say no to the term – change the classification on the content object 12 Karen Loasby 7 March 2005

Slide 13: 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 13 Karen Loasby 7 March 2005

Slide 14: 0 100 200 300 400 500 600 700 800 cumbria tyne cambridgeshire guernsey leicester south yorkshire wiltshire suffolk liverpool manchester berkshire bristol kent coventry tees jersey Karen Loasby stoke & staffs nottingham between teams derby humber somerset northamptonshire Graph showing variation norfolk beds, bucks & herts leeds hereford & worcs birmingham 7 March 2005 14

Slide 15: 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 15 Karen Loasby 7 March 2005

Slide 16: Growth of facets CV Requests By Month 7000 6000 5000 Quantity Name Location 4000 Subject 3000 BBC Brand Time Period 2000 1000 0 e y t l v ly c g t ri p c a n o e u u p e O u M N D A J A S J M onth 16 Karen Loasby 7 March 2005

Slide 17: 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 17 Karen Loasby 7 March 2005

Slide 18: Resourcing Handling the requests from journalists • First 3 months – one IA • • Subsequently 2 to 3 junior IAs • Too much – how to reduce? 18 Karen Loasby 7 March 2005

Slide 19: 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 19 Karen Loasby 7 March 2005

Slide 20: 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 20 Karen Loasby 7 March 2005

Slide 21: Example Subject: Pregnancy 21 Karen Loasby 7 March 2005

Slide 22: 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 22 Karen Loasby 7 March 2005

Slide 23: Lessons learnt • Number of terms suggested depends on – Type of facet – Dynamism of content – Scope of the content – Enthusiasm of users 23 Karen Loasby 7 March 2005

Slide 24: Next? • High value facets still need control – Make use of the metadata(!) – Sell the message – Federated management – Earlier in production • And for folk tagging? 24 Karen Loasby 7 March 2005

Slide 25: Thanks to the IA team for their analysis work; – Jon Carey – Adil Hussein – Christine Rimmer 25 Karen Loasby 7 March 2005

Slide 26: Thank you Questions or comments? Karen Loasby karen.loasby@bbc.co.uk 26 Karen Loasby 7 March 2005