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Aol dam taxonomy

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Aol dam taxonomy

  1. 1. Taxonomy at AOL<br />Classifying the parts of a whole<br />Noel Agnew (@noelagnewny)<br />Ashley Marty (@ashleykmarty)<br />June 09, 2011<br />
  2. 2. The problem:Aol did not have a common vocabulary<br />
  3. 3. 56+ Media brands, including:<br />DAM New York 2011<br />Page 3<br />
  4. 4. Multiple ad systems and content platforms<br />Content platforms:<br />Blogsmith<br />Huffington Post (Movable type)<br />5min<br />Truveo<br />StudioNow<br />DAM New York 2011<br />Page 4<br />Some ad systems:<br />AdTech<br />Advertising.com<br />Feedpoint/Dynamic Banners<br />
  5. 5. All speaking different languages…<br />DAM New York 2011<br />Page 5<br />Tag.aol.com “beyonce”<br />Tag… “beyonceknowles”<br />AOL Music “beyonce”<br />AOL music “beyonceknowles”<br />Moviefone “beyonceknowles”<br />Huffington Post “beyonce”<br />H… Post “beyonceknowles”<br />
  6. 6. What we were asked to do<br />Effectively and granularly classify content:<br /> For improved ad sales<br /> To relate content within and between the brands<br /> In some cases, to assist editors with external-facing tags<br /> All sorts of other bits of magic (which will be touched on later)<br />DAM New York 2011<br />Page6<br />
  7. 7. The solution:Classify all AOL content in the same way<br />
  8. 8. Faceted Ontology<br />DAM New York 2011<br />Page 8<br />“…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. ”<br />-M.K. Bergman, “An Executive Intro to Ontologies” http://www.mkbergman.com/900/an-executive-intro-to-ontologies/<br />
  9. 9. Subjects<br />We have approx. 6800 subjects<br />Generally hierarchical, but some associative relationships<br />Iterative process with editors (subject specialists)<br />12 Top levels (or classes)<br />DAM New York 2011<br />Page 9<br />Arts and Humanities<br />Education<br />Entertainment<br />Health and Medicine<br />Lifestyle<br />Money and Finance<br />News and Politics<br />Science and Tech<br />Social Sciences<br />Sports<br />Transportation<br />Travel and Tourism<br />
  10. 10. Entities<br />Named Things (includes persons)<br />Locations<br />Works<br />Events<br />Groups<br />Brands<br />Products<br />DAM New York 2011<br />Page 10<br />Proper nouns (specific persons, places, things)<br />Not hierarchical, but rather associative relationships<br />7 Entities Vocabularies<br />
  11. 11. Taxonomy/ontology mashup<br />DAM New York 2011<br />Page 11<br />Sprint<br />HTC Evo 4G<br />OSX<br />iPhone<br />Verizon<br />Apple<br />AT&T<br />
  12. 12. Making it work<br />
  13. 13. HELLO TEL AVIV!<br />When we were tasked with this, we had very little direct communication with the team in Tel Aviv that runs the classification engine…<br />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!<br />DAM New York 2011<br />Page 13<br />
  14. 14. Train in vain?<br />DAM New York 2011<br />Page 14<br />‘Women's Shoes’<br />We had to find training data for each subject in the taxonomy… and are continually doing so to improve classification. <br />
  15. 15. DAM New York 2011<br />Page 15<br />More Contact with the Classification Team<br /> Providing Feedback on tagging results<br /> Collaborating on priorities<br /> What data is most valuable to the tagger?<br />Getting to Know You<br />
  16. 16. Turning large amounts of data into an ontology<br />DAM New York 2011<br />Page 16<br />More data sources means multiple records for the same Entity<br />More sources = More effort required in Merging records<br />Name: Beyoncé<br />MusicPerson<br />MoviePerson<br />Alias (synonym): Beyonce Knowles<br />Alias (synonym): Beyonce<br />Source:Wikipedia<br />Source: AolMusicDB<br />Source: AolMovieDB<br />After Merge, one record remains with metadata and relationships from all sources<br />More sources = More valuable records<br />
  17. 17. Where we are now<br />
  18. 18. DAM New York 2011<br />Page 18<br />Integrating with Advertising systems<br />Our subjects can be mapped to Advertising categories to serve ads for related products<br />Current Department Store campaign: <br />Page 18<br />
  19. 19. Recommending Tags for Editorial<br />DAM New York 2011<br />Page 19<br />
  20. 20. Where we’re going<br />
  21. 21. On the Roadmap…<br />More projects with Advertising teams<br />More data in our ontology to make classification better<br />Refining the ontology- because it’s a living thing<br />DAM New York 2011<br />Page 21<br />
  22. 22. Lessons learned<br />
  23. 23. Life lessons…<br />Keep your eye on the prize<br />Expect people to think this is a much smaller task than it is<br />Don’t reinvent the wheel<br />Never underestimate the power of the ability to manipulate data<br />DAM New York 2011<br />Page 23<br />