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What a long, strange trip it’s been

What a long, strange trip it’s been. R.V.Guha Google. Outline of talk. The context How did we end up where we are Schema.org What it is, status of adoption Schema.org principles, how does it work Looking ahead Next Generation Applications. About 18 years ago, ….

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What a long, strange trip it’s been

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  1. What a long, strange trip it’s been R.V.Guha Google schema.org

  2. Outline of talk • The context • How did we end up where we are • Schema.org • What it is, status of adoption • Schema.org principles, how does it work • Looking ahead • Next Generation Applications schema.org

  3. About 18 years ago, … • People started thinking about structured data on the web • A few people from Netscape, Microsoft and W3C got together @MIT • Trying to make sense of a flurry of activity/proposals • XML, MCF, CDF, Sitemaps, … • There were a number of problems • PICS, Meta data, sitemaps, … • But one unifying idea schema.org

  4. Context: The Web for humans Structured Data Web server HTML schema.org

  5. Goal: Web for Machines & Humans Structured Data Web server Apps schema.org

  6. What does that mean? Ryan, Oklahama Actor type birthplace Chuck Norris birthdate March 10th 1940 • Notable points • - Graph Data Model • - Common Vocabulary schema.org

  7. How do we get there? • How does the author give us the graph • Data Model: Graph vs tree vs … • Syntax • Vocabulary • Identifiers for objects • Why should the author give us the graph? schema.org

  8. Going depth first • Many heated battles • Lot of proposals, standards, companies, … • Data model • Trees vs DLGs vs Vertical specific vs who needs one? • Syntax • XML vs RDF vsjsonvs… • Model theory anyone • We need one vs who cares vs what’s that? schema.org

  9. Timeline of ‘standards’ • ‘96: Meta Content Framework (MCF) (Apple) • ’97: MCF using XML (Netscape)  RDF, CDF • ’99 -- : RDF, RDFS • ’01 -- : DAML, OWL, OWL EL, OWL QL, OWL RL • ’03: Microformats • And many many many more … SPARQL, Turtle, N3, GRDDL, R2RML, FOAF, SIOC, SKOS, … • Lots of bells & whistles: model theory, inference, type systems, … schema.org

  10. But something was missing … • Fewer than 1000 sites were using these standards • Something was clearly missing and it wasn’t more language features • We had forgotten the ‘Why’ part of the problem • The RSS story schema.org

  11. ’07 - :Rise of the consumers • Yahoo! Search Monkey, Google Rich Snippets, Facebook Open Graph • Offer webmasters a simple value proposition • Search engines to webmasters: • You give us data … we make your results nicer • Usage begins to take off • 1000x increase in markup’ed up pages in 3 years schema.org

  12. Yahoo Search Monkey • Give websites control over snippet presentation • Moderate adoption • Targeted at high end developers • Too many choices schema.org

  13. Google Rich Snippets: Reviews schema.org

  14. Google Rich Snippets: Events schema.org

  15. Google Rich Snippets • Multi-syntax • Adhoc vocabulary for each vertical • Very clear carrot • Lots of experimentation on UI • Moderately successful: 10ks of sites • Scaling issues with vocabulary schema.org

  16. Situation in 2010 • Too many choices/decisions for webmasters • Divergence in vocabularies • Too much fragmentation • N versions of person, address, … • A lot of bad/wrong markup • ~25% for micro-formats, ~40% with RDFA • Some spam, mostly unintended mistakes • Absolute adoption numbers still rather low • Less than 100k sites schema.org

  17. Schema.org • Work started in August 2010 • Google, Yahoo!, Microsoft & then Yandex • Goals: • One vocabulary understood by all the search engines • Make it very easy for the webmaster • It is A vocabulary. Not The vocabulary. • Webmasters can use it together other vocabs • We might not understand the other vocabs. Others might schema.org

  18. Schema.org: Major sites • News: Nytimes, guardian.com, bbc.co.uk, • Movies: imdb, rottentomatoes, movies.com • Jobs / careers: careerjet.com, monster.com, indeed.com • People: linkedin.com, • Products: ebay.com, alibaba.com, sears.com, cafepress.com, sulit.com, fotolia.com • Videos: youtube, dailymotion, frequency.com, vinebox.com • Medical: cvs.com, drugs.com • Local: yelp.com, allmenus.com, urbanspoon.com • Events: wherevent.com, meetup.com, zillow.com, eventful • Music: last.fm, myspace.com, soundcloud.com schema.org

  19. Schema.org principles: Simplicity • Simple things should be simple • For webmasters, not necessarily for consumers of markup • Webmasters shouldn’t have to deal with N namespaces • Complex things should be possible • Advanced webmasters should be able to mix and match vocabularies • Syntax • Microdata, usability studies • RDFa, json-ld, … schema.org

  20. Schema.org principles: Simplicity • Can’t expect webmasters to understand Knowledge Representation, Semantic Web Query Languages, etc. • It has to fit in with existing workflows • A posteriori ‘markup tools’ don’t work • Avoid KR system driven artifacts • Multiple domain / range for attributes • No classes like ‘Agent’ • Categories and attributes should be concrete schema.org

  21. Schema.org principles: Simplicity • Copy and edit as the default mode for authors • It is not a linear spec, but a tree of examples • Vocabularies • Authors only need to have local view • But schema.org tries to have a single global coherent vocabulary schema.org

  22. Schema.org principles: Incremental • Started simple • ~ 100 categories at launch • Applies to every area • Add complexity after adoption • now ~1200 vocab items • Go back and fill in the blanks • Move fast, accept mistakes, iterate fast schema.org

  23. Schema.org Principles: URIs • ~1000s of terms like Actor, birthdate • ~10s for most sites • Common across sites • ~10ks of terms like USA • External enumerations • ~1b-100b terms like Chuck Norris and Ryan, Oklahama • Cannot expect agreement on these • Reference by description • Consumers can reconcile entity references Ryan, Oklahama Actor type birthplace Chuck Norris birthdate citizenOf March 10th 1940 USA schema.org

  24. A city named Ryan In the state OK An Actor named Chuck Norris birthplace + spouse An Actor named Chuck Norris birthdate A Person named GeenaO’Kelley March 10th 1940 citizenOf Ryan, Oklahama Actor birthdate = type USA birthplace March 10th 1940 Chuck Norris spouse GeenaO’Kelley birthdate citizenOf USA March 10th 1940 schema.org

  25. Schema.org Principles: Collaborations • Most discussions on public W3C lists • Work closely with interest communities • Work with others to incorporate their vocabularies • We give them attribution on schema.org • Webmasters should not have to worry about where each piece of the vocabulary came from • Webmasters can mix and match vocabs schema.org

  26. Schema.org Principles: Collaborations • IPTC /NYTimes / Getty with rNews • Martin Hepp with Good Relations • US Veterans, Whitehouse, Indeed.com with Job Posting • Creative Commons with LRMI • NIH National Library of Medicine for Medical vocab. • Bibextend, HighwirePress for Bibliographic vocabulary • Benetech for Accessibility • BBC, European Broadcasting Union for TV & Radio schema • Stackexchange, SKOS group for message board • Lots and lots and lots of individuals schema.org

  27. Schema.org Principles: Partners • Partner with Authoring platforms • Drupal, Wordpress, Blogger, YouTube • Drupal 8 • Schema.org markup for many types • News articles, comments, users, events, … • More schema.org types can be created by site author • Markup in HTML5 & RDFa Lite • Will comeout early 2015 schema.org

  28. Recent Additions • From Nouns to Verbs: Actions • Object  potential actions • Constraints on actions • E.g., ThorMovie  Stream, Buy, … • Introducing time: Roles • E.g., Joe Montana played for the SF 49ers from 1979 to 1992 in the position QuarterBack schema.org

  29. Recent Additions • Scholarly work, Comics, Serials, … • Communications: TV, Radio, Q&A, … • Accessibility • Commerce: Reservations • Sports • Buyer/Seller, etc. • Bibtex • The ontology is growing … • ~800 properties • ~600 classes schema.org

  30. Looking forward • Schema.org is doing better than we expected • Thanks to millions of webmasters! • But this is not the final goal • Just the means to the next generation of applications • First generation of applications • Rich presentation of search results • Many new applications • Related to search and beyond schema.org

  31. Newer Applications: Knowledge Graph schema.org

  32. Newer Applications: Knowledge Graph schema.org

  33. Non search applications: Google Now User profile (google.com/now/topics) + structured data feeds schema.org

  34. Pinterest: Schema.org for Rich Pins schema.org

  35. Reservations  Personal Assistant • Open Table website  confirmation email  Android Reminder schema.org

  36. Vertical Search • Structured data in search • Web search: annotate search results OR • Filtering based on structured data • Only in specialized corpus • Ecommerce, real estate, etc. • How about filtering based on structured data across the web? schema.org

  37. Google Rich Snippets: Recipe View schema.org

  38. Searching for Veteran friendly jobs Web scale vertical search schema.org

  39. Web Scale custom vertical search • Build your own custom vertical search engine • Google does the heavy lifting: crawling, indexing, etc. • You specify the schema.org restricts • APIs to help build your own UI • Searches over all pages on the web with a certain schema.org markup • Demo schema.org

  40. Scientific Data Publishing • US Govt alone spends over $60B/yr on scientific research • Primary output of most of this research is data • Most of the data is thrown away • All that is published are papers • We would like the data published in a easily reusable form schema.org

  41. Case study: Clinical Trials • Clinical trials • 4000+ clinical trials at any time in the US alone • Almost all the data ‘thrown away’ • All that gets published is a textual ‘abstract’ • Many of the trials are redundant • Earlier trials have the data • Assumptions, etc. cannot be re-examined • Longitudinal studies extremely hard, but super important • Having all the clinical trial data on the web, in a common schema will make this much easier! schema.org

  42. Case study: SkyServer • Huge amount of astronomy data • Jim Gray, NASA and others brought it all together, normalized it and made it available on the web • Has changed the way astronomy research takes place • Students in Africa getting PhDs without leaving Africa! • Radio/Ultra-violet/Visible light data easily brought together • Caveats • SQL biased, not distributed, not scalable • All normalization done by hand, once • Small number of data sources • But shows that it can be done … schema.org

  43. First steps for scientific data publication • OPTC directive for data from federally funded research to be freely available • Formation of new ‘Data Science’ institute inside NIH • Seeing traction in scientific data on the web • Lot of interest in creating schemas • Public repositories for scientific data starting schema.org

  44. Concluding • Structured data on the web is now ‘web scale’ • Schema.org has got traction and is evolving • The most interesting applications are yet to come schema.org

  45. Questions? schema.org

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