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Can SPARQL be fun? Explore & query with vinge tutorial

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If you or your team write SPARQL queries and are sometimes wondering if there is a query builder which allows you to overcome two major obstacles why non-programmer people aren’t querying Linked Data, namely 1. hard to understand what data is available, how the classes and properties are named, what the relations are 2. demand to know SPARQL language syntax and whole concept of triple based binding and graph matching then you perhaps would like to check out what Vinge Free has come up with. We have launched a Free Edition of a new kind of Linked Data Browser which we call “Explore & Query”. It is available for download and there are two videos that demonstrate the tool in action. http://www.vingefree.com/querybyexplore/ On a nutshell it is a combination of a Linked Data browser and a Query Builder. It allows to explore and reveal data models; build, visualize and execute SPARQL queries. So essentially while you are navigating in the linked data graph, the tool builds the query for you. On any graph. Regardless how expressive is the ontology or if it is even published. And it is fun! The tool works over any SPARQL endpoint using HTTP binding, which means it runs on top of any triple store.

Next slideshare on Explore&Query topic: http://www.slideshare.net/JrgenKerstna/failing-fast-with-explorequery

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Can SPARQL be fun? Explore & query with vinge tutorial

  1. 1. Explore & Query Tutorial © 2013 Vinge Free AB, Sweden
  2. 2. Find in DBpedia (Wikipedia content) All soccer players, who played as goalkeeper for a club that has a stadium with more than 40.000 seats and who are born in a country with more than 10 million inhabitants … and who has also scored © 2013 Vinge Free AB, Sweden
  3. 3. The goal is to write the SPARQL query © 2013 Vinge Free AB, Sweden
  4. 4. Your options 1. Start typing in SPARQL editor © 2013 Vinge Free AB, Sweden What are the options?
  5. 5. Your options 2. Use visual Explore & Query by Vinge which is downloadable from http://www.vingefree.com/querybyexplore © 2013 Vinge Free AB, Sweden
  6. 6. Where to start? You have 3 options for how to get in there: ● By searching for instances of things you know should exist in the data. This is a free text search, like Google. From hit list select one instance and start browsing relations and explore the model. ● Searching for concept or class of instances, then selecting one class and listing the instances of it. Select one instance and start exploring. ● Combination of the two above - to limit the text search within the concept and dataset. © 2013 Vinge Free AB, Sweden “I remember the German goalkeeper Harald Schumacher” “Soccer player is the main concept in my query!”
  7. 7. List of search results Select Harald_Schumacher and enter the Linked Data browser © 2013 Vinge Free AB, Sweden
  8. 8. Explore the data Select the type and properties of interest: add or navigate to © 2013 Vinge Free AB, Sweden
  9. 9. Data model being revealed Navigate to FC Bayern München where he played. © 2013 Vinge Free AB, Sweden
  10. 10. Data model being revealed By default the club relation is not restricted, it can be anything. To limit the query to only soccer clubs add specific type i.e. SoccerClub in this case. © 2013 Vinge Free AB, Sweden
  11. 11. Data model being revealed Capacity seems to mean # of seats on the stadium, so it is relevant - add it ... © 2013 Vinge Free AB, Sweden
  12. 12. Data model being revealed © 2013 Vinge Free AB, Sweden Time to filter 40,000 seats ...
  13. 13. Data model being revealed Time to set the filter ... © 2013 Vinge Free AB, Sweden
  14. 14. While Exploring... you have been generalizing. The graph that matches Harald Schumacher is applied to match similar “things” described by same properties and relations. © 2013 Vinge Free AB, Sweden
  15. 15. You can test your generalization The SPARQL query is generated and executed © 2013 Vinge Free AB, Sweden
  16. 16. Explore more - identify goalkeepers The “position” attribute seems to contain this information. © 2013 Vinge Free AB, Sweden
  17. 17. Explore more - identify goalkeepers Facet is inferred on the fly. Select from it. © 2013 Vinge Free AB, Sweden
  18. 18. Explore more - identify goalkeepers And that was a bit of the guesswork... © 2013 Vinge Free AB, Sweden
  19. 19. Now we only have goalies in the result set. Explore more - identify goalkeepers © 2013 Vinge Free AB, Sweden
  20. 20. Navigate to birth country and filter 10 million Navigate to goals and set filter >0 The next slide will show the revealed model. Exercise - finish it up yourself © 2013 Vinge Free AB, Sweden
  21. 21. Do you see the same graph? Probably not. On the next slide we guess why not... Navigation Map to answer the initial question © 2013 Vinge Free AB, Sweden
  22. 22. The country which does not exist today and does not have population data available. For not to miss out players from dissolved countries, we navigate via birthplace (i.e. town) that seems to link to their current countries. Harald Schumacher was born in West Germany © 2013 Vinge Free AB, Sweden See how we did it
  23. 23. Harald Schumacher was born in Düren Replay © 2013 Vinge Free AB, Sweden
  24. 24. Düren is a Town. But we don’t want to exclude country boys from the query. Replay © 2013 Vinge Free AB, Sweden
  25. 25. Düren is in Germany. Replay © 2013 Vinge Free AB, Sweden
  26. 26. Germany is a Country. Replay © 2013 Vinge Free AB, Sweden
  27. 27. There is a population attribute for Germany. Replay © 2013 Vinge Free AB, Sweden
  28. 28. Add filter to population. Replay © 2013 Vinge Free AB, Sweden
  29. 29. Navigation Map to answer the initial question. Replay © 2013 Vinge Free AB, Sweden
  30. 30. Dealing with bad data in the graph will be the subject on another occasion. In order to answer the exact question - some fine-tuning is needed. The query to answer the initial question © 2013 Vinge Free AB, Sweden
  31. 31. In the query editor generate the query and select the columns for result set. Visualize Query to check that the binding graph is connected. The query to answer the initial q © 2013 Vinge Free AB, Sweden
  32. 32. This is the view to see what nodes in the graph are selected to the result set table. There must not be disconnected subgraphs or the result set will be a cartesian product of unrelated nodes. Verify the query graph © 2013 Vinge Free AB, Sweden
  33. 33. Source data contained multiple attributes for goals if scored for different teams. To get the list of distinct players and total # of goals, the query needs to be fine-tuned in the editor. Deal with duplicates © 2013 Vinge Free AB, Sweden
  34. 34. Source data contained multiple attributes for goals if scored for different teams. To get list of distinct players and total # of goals, the query needs to be modified in the interactive and context sensitive editor. Eventually some SPARQL is needed © 2013 Vinge Free AB, Sweden
  35. 35. The answer to the initial question © 2013 Vinge Free AB, Sweden Wow!
  36. 36. with Sgvizler And why not to do more ... © 2013 Vinge Free AB, Sweden
  37. 37. © 2013 Vinge Free AB, Sweden download from http://www.vingefree.com/querybyexplore

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