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

1. Explore & Query
Tutorial
© 2013 Vinge Free AB, Sweden

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. The goal is to write the SPARQL query
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4. Your options
1. Start typing in SPARQL editor
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What are the options?

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. 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. List of search results
Select Harald_Schumacher and enter the Linked Data
browser
© 2013 Vinge Free AB, Sweden

8. Explore the data
Select the type and properties of interest: add or navigate to
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9. Data model being revealed
Navigate to FC Bayern München where he played.
© 2013 Vinge Free
AB, Sweden

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.
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AB, Sweden

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. Data model being revealed
© 2013 Vinge Free
AB, Sweden
Time to filter 40,000 seats ...

13. Data model being revealed
Time to set the filter ...
© 2013 Vinge Free
AB, Sweden

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.
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AB, Sweden

15. You can test your generalization
The SPARQL query is generated and executed
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AB, Sweden

16. Explore more - identify goalkeepers
The “position” attribute seems to contain this
information.
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AB, Sweden

17. Explore more - identify goalkeepers
Facet is inferred on the fly. Select from it.
© 2013 Vinge Free
AB, Sweden

18. Explore more - identify goalkeepers
And that was a bit of the guesswork...
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AB, Sweden

19. Now we only have goalies in the result set.
Explore more - identify goalkeepers
© 2013 Vinge Free
AB, Sweden

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. Do you see the same graph? Probably not.
On the next slide we guess why not...
Navigation Map to answer the initial
question
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AB, Sweden

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. Harald Schumacher was born in Düren
Replay
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AB, Sweden

24. Düren is a Town. But we don’t want to exclude country boys from the query.
Replay
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AB, Sweden

25. Düren is in Germany.
Replay
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AB, Sweden

26. Germany is a Country.
Replay
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AB, Sweden

27. There is a population attribute for Germany.
Replay
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AB, Sweden

28. Add filter to population.
Replay
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AB, Sweden

29. Navigation Map to answer the initial question.
Replay
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AB, Sweden

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. 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. 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. 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. 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. The answer to the initial question
© 2013 Vinge Free
AB, Sweden
Wow!

36. with Sgvizler
And why not to do more ...
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AB, Sweden

37. © 2013 Vinge Free
AB, Sweden
download from
http://www.vingefree.com/querybyexplore