More Related Content


DBpedia's Triple Pattern Fragments

  1. DBpedia's
 Triple Pattern Fragments Ruben Verborgh
  2. Building applications with
 Linked Data from the Web
 should become as realistic
 and easy as using Web APIs.
  3. <a  class="twitter-­‐timeline"      href=""      data-­‐widget-­‐id="YOUR-­‐WIDGET-­‐ID-­‐HERE">   Tweets  by  @twitterdev   </a>   <script>   window.twttr=(function(d,s,id){var   js,fjs=d.getElementsByTagName(s)[0],   t=window.twttr||{};   if(d.getElementById(id))return;
 js=d.createElement(s);;js.src=   "";   fjs.parentNode.insertBefore(js,fjs);   t._e=[];t.ready=function(f) {t._e.push(f);};return  t;} (document,”script","twitter-­‐wjs"));   </script> 30 Followers you know Tweet to Message Tomasz Pluskiewicz @tpluscode · Jan 28 LinkedDataFragments retweeted I finally really understood @LDFragments… via @videolectures 2 2 Miel Vander Sande @Miel_vds · Jan 23 LinkedDataFragments retweeted There’s a @LDFragments #Java client & server Under develop, help welcome! #semweb #LinkedData View summary2 4 Ruben Verborgh @RubenVerborgh · Jan 22 LinkedDataFragments retweeted My #ISWC2014 talk “Querying datasets on the Web with high availability”: explains #RDF #API trade-offs.… @LDFragments 3 9 Ruben Verborgh @RubenVerborgh · Jan 12 LinkedDataFragments retweeted “The @LDFragments server design lets us publish and query @OrgRef data using a free Heroku instance.” —@tedlawless… 1 2 Ted Lawless @tedlawless · Jan 11 LinkedDataFragments retweeted Notebook: mapping @OrgRef to RDF and publishing with @LDFragments. Next up matching to @VIVOcollab instances.… 9 11 LinkedDataFragments @LDFragments · Dec 17 First Triple Pattern Fragments server running on Heroku, set up by @tedlawless.… #affordable #LinkedData View summary1 2 LinkedDataFragments @LDFragments · Nov 26 Querying Linked Data in HTML5 has never been easier: via a simple tag, you can query in a streaming or polling way … View summary5 9
  4. The easy part is covered. All triples look the same:
 subject – predicate – object. That’s a major advantage
 over 15.000+ different APIs.
  5. But is it also realistic? <95% MORE THAN HALF
 of public SPARQL endpoints AVAILABILITY Buil-Aranda – Hogan – Umbrich – Vandenbussche
 SPARQL Web-Querying Infrastructure: Ready for Action?
  6. We design simpler server interfaces
 to publish Linked Data at low cost, making clients solve complex queries. We host a DBpedia interface, and hope to inspire you to
 build cool DBpedia applications.
  7. About DBpedia’s fragments Usage analysis so far Fragments in the future
  8. About DBpedia’s fragments Usage analysis so far Fragments in the future
  9. Classic Linked Data publishing:
 simple client, hard-working server. client server SELECT * WHERE { ?person a dbpedia-owl:Writer; rdfs:label ?name; dbpedia-owl:almaMater[ rdfs:label "Trinity College, Dublin"@en ]. FILTER LANGMATCHES(LANG(?name), "EN") }
  10. SPARQL endpoints have to work hard
 in comparison to other servers. server highly individualized requests low cacheability large per-request processing cost
  11. SPARQL endpoints might bring
 high costs for possibly low gain. server “Fine, we’ll publish a data dump.” Data dumps don’t allow
 Web applications on live data. You already provide data for free,
 is it realistic to pay for users’ queries, too?
  12. There’s more to Linked Data publishing
 than just the two extremes. data
 dump SPARQL
 endpoint high server efforthigh client effort Triple Pattern
 Fragments server
  13. Future Linked Data publishing:
 simple servers, clever clients. client server ?person a dbpedia-owl:Writer. Triple Pattern
  14. Triple Pattern Fragment servers
 cannot work hard, by definition. server highly reusable requests high cacheability low per-request processing cost
  15. Complex queries are efficiently
 executed on the client-side. client SELECT * WHERE { ?person a dbpedia-owl:Writer; rdfs:label ?name; dbpedia-owl:almaMater dbpedia:Trinity_College_Dublin. } Query now at In your browser—in pure JavaScript!
  16. About DBpedia’s fragments Usage analysis so far Fragments in the future
  17. data (paged) controls (other fragments) metadata (total count)
  18. Pingdom 155.702 Chrome 285.385 GoogleBot 332.177 TPF Client 3.219.756 The Client for Node.js is leading,
 but other bots (and humans) follow.
  19. JSON 2.867 283.035 HTML 316.183 TriG 833.341 Turtle 2.810.532 (anything) Turtle has been most popular,
 but TriG will lead soon.
  20. Expired 164.105 Hit 1.249.571 Miss 2.838.720 More than a fourth of all requests
 was served directly from the cache.
  21. Oct 2014 Nov 2014 Dec 2014 Jan 2015 Feb 2015 267.196 1.038.867 357.305 2.433.045 157.805 in 4 days 17 Oct 2014 – 4 Feb 2015 November was a very busy month;
 but February might top that!
  22. “Type“ fragments were most common,
 then “all”, and specific subclasses. ?s rdf:type ?o ?s ?p ?o <s> rdfs:subClassOf ?o 111.773 148.411 155.706
  23. DBpedia fragments had 99.99% uptime,
 having < 5 mins downtime per month. 1 request every minute by Pingdom 20 did not return 5 of which due to planned maintenance
  24. About DBpedia’s fragments Usage analysis so far Fragments in the future
  25. We plan to extend the interface
 with features that improve querying. data
 dump SPARQL
 endpoint high server efforthigh client effort Triple Pattern
  26. APPS No more excuses—start building!
 DBpedia is queryable and 99.9% up. It’s time to make on top of live DBpedia data.
  27. Instead of making apps with Web APIs
 we’ll build apps from Linked Data. APPS WEB on top of live DBpedia data.
  28. Dublin SPARQL
 endpoint Dublin The classical approach:
 we ask, we wait, we act.
  29. Dublin DublinDublin The Web approach:
 we ask—and act as results arrive.
  30. by iMinds – Ghent University
  31. by Michael Luggen
  32. your app? client = new ldf.FragmentsClient(''); stream = new ldf.SparqlIterator(query, { fragmentsClient: client }); stream.on('data', doSomethingNice); easy & realistic 3 lines of JavaScript & fun
  33. @RubenVerborgh