AOL did not have a common vocabulary across its 56+ media brands and content platforms which made content difficult to classify and relate. To address this, they created a faceted ontology with over 6,800 subjects and 7 entity vocabularies to classify all AOL content in the same way. They had to train the classification engine by providing training data for each subject. In the future, they aim to integrate more with advertising systems, provide better recommendations to editors, and continue refining the ontology.

1. Taxonomy at AOL Classifying the parts of a whole Noel Agnew (@noelagnewny) Ashley Marty (@ashleykmarty) June 09, 2011

2. The problem:Aol did not have a common vocabulary

3. 56+ Media brands, including: DAM New York 2011 Page 3

4. Multiple ad systems and content platforms Content platforms: Blogsmith Huffington Post (Movable type) 5min Truveo StudioNow DAM New York 2011 Page 4 Some ad systems: AdTech Advertising.com Feedpoint/Dynamic Banners

5. All speaking different languages… DAM New York 2011 Page 5 Tag.aol.com “beyonce” Tag… “beyonceknowles” AOL Music “beyonce” AOL music “beyonceknowles” Moviefone “beyonceknowles” Huffington Post “beyonce” H… Post “beyonceknowles”

6. What we were asked to do Effectively and granularly classify content: For improved ad sales To relate content within and between the brands In some cases, to assist editors with external-facing tags All sorts of other bits of magic (which will be touched on later) DAM New York 2011 Page6

7. The solution:Classify all AOL content in the same way

8. Faceted Ontology DAM New York 2011 Page 8 “…structural frameworks for organizing information on the semantic Web and within semantic enterprises. They provide unique benefits in discovery, flexible access, and information integration due to their inherent connectedness; that is, their ability to represent conceptual relationships. ” -M.K. Bergman, “An Executive Intro to Ontologies” http://www.mkbergman.com/900/an-executive-intro-to-ontologies/

9. Subjects We have approx. 6800 subjects Generally hierarchical, but some associative relationships Iterative process with editors (subject specialists) 12 Top levels (or classes) DAM New York 2011 Page 9 Arts and Humanities Education Entertainment Health and Medicine Lifestyle Money and Finance News and Politics Science and Tech Social Sciences Sports Transportation Travel and Tourism

10. Entities Named Things (includes persons) Locations Works Events Groups Brands Products DAM New York 2011 Page 10 Proper nouns (specific persons, places, things) Not hierarchical, but rather associative relationships 7 Entities Vocabularies

11. Taxonomy/ontology mashup DAM New York 2011 Page 11 Sprint HTC Evo 4G OSX iPhone Verizon Apple AT&T

12. Making it work

13. HELLO TEL AVIV! When we were tasked with this, we had very little direct communication with the team in Tel Aviv that runs the classification engine… We also were under the impression that auto-classification was their issue and they’d just have to classify with whatever we gave them. This was WRONG! DAM New York 2011 Page 13

14. Train in vain? DAM New York 2011 Page 14 ‘Women's Shoes’ We had to find training data for each subject in the taxonomy… and are continually doing so to improve classification.

15. DAM New York 2011 Page 15 More Contact with the Classification Team Providing Feedback on tagging results Collaborating on priorities What data is most valuable to the tagger? Getting to Know You

16. Turning large amounts of data into an ontology DAM New York 2011 Page 16 More data sources means multiple records for the same Entity More sources = More effort required in Merging records Name: Beyoncé MusicPerson MoviePerson Alias (synonym): Beyonce Knowles Alias (synonym): Beyonce Source:Wikipedia Source: AolMusicDB Source: AolMovieDB After Merge, one record remains with metadata and relationships from all sources More sources = More valuable records

17. Where we are now

18. DAM New York 2011 Page 18 Integrating with Advertising systems Our subjects can be mapped to Advertising categories to serve ads for related products Current Department Store campaign: Page 18

19. Recommending Tags for Editorial DAM New York 2011 Page 19

20. Where we’re going

21. On the Roadmap… More projects with Advertising teams More data in our ontology to make classification better Refining the ontology- because it’s a living thing DAM New York 2011 Page 21

22. Lessons learned

23. Life lessons… Keep your eye on the prize Expect people to think this is a much smaller task than it is Don’t reinvent the wheel Never underestimate the power of the ability to manipulate data DAM New York 2011 Page 23