Diese Präsentation wurde erfolgreich gemeldet.
Wir verwenden Ihre LinkedIn Profilangaben und Informationen zu Ihren Aktivitäten, um Anzeigen zu personalisieren und Ihnen relevantere Inhalte anzuzeigen. Sie können Ihre Anzeigeneinstellungen jederzeit ändern.

Semantic Web and Schema.org

109.307 Aufrufe

Veröffentlicht am

Talk given at SemTech 2014 (and earlier, at ISWC 2013) on the evolution of the Semantic Web and Schema.org

Veröffentlicht in: Internet, Software, Technologie
  • Hello! Get Your Professional Job-Winning Resume Here - Check our website! https://vk.cc/818RFv
       Antworten 
    Sind Sie sicher, dass Sie …  Ja  Nein
    Ihre Nachricht erscheint hier

Semantic Web and Schema.org

  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? birthplace Chuck Norris Ryan, Oklahama birthdate March 10th 1940 Actor type - 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 vs json vs … • 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 Chuck Norris birthplace • ~1b-100b terms like Chuck Norris and Ryan, Oklahama – Cannot expect agreement on these – Reference by description – Consumers can reconcile entity references Ryan, Oklahama March 10th 1940 Actor type citizenOf USA birthdate schema.org
  24. An Actor named Chuck Norris March 10th 1940 citizenOf USA birthdate A city named Ryan In the state OK birthplace birthdate March 10th 1940 An Actor named Chuck Norris + spouse A Person named Geena O’Kelley = Chuck Norris USA Ryan, Oklahama birthplace spouse March 10th 1940 Actor type citizenOf birthdate Geena O’Kelley 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, Highwire Press 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 come out 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. Web scale vertical search • Searching for Veteran friendly jobs 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

×