Hi Nandana,
very nice, indeed. Are you planning to make it available as Open Source.
Thus one could also install it locally for private datasets.
+1 for using ElasticSearch and Docker
However, what is your experience with using ElasticSearch for triple
indexing? Why did you not use a triple store?
Best Regards,
Olaf
Am 08.10.2015 um 18:09 schrieb Nandana Mihindukulasooriya:
Hi all,
We are developing a tool called Loupe [ http://loupe.linkeddata.es ] for
inspecting and exploring datasets to understand which vocabularies
(classes, and properties) are used in a dataset and which are common
triple patterns. Loupe has some similarities to LODStat, Aether,
ProLOD++, etc. but it provides the ability to dig into more details. It
also connects the information provided directly to data so that so that
one can see the triples that correspond to those numbers. At the
moment, it indexes 2+ billion triples from datasets including DBpedia
(17 languages), wikidata, Linked Brainz, Bio models, etc.
It's easier to describe what information Loupe provides using an
example. If we take the DBpedia dataset, first it provides a summary
with the number of triples, distinct subjects, objects, their
composition (IRIs, blank nodes, literals), etc. and summary of the other
information that we will present below. http://tinyurl.com/loupe-dbpedia
The class explorer provides the list of 941 classes used, number of
instances per each class, number classes in each namespace etc. It also
allows you to search for classes. http://tinyurl.com/dbpedia-classes
If we select a concrete class such as dbo:Person, it shows the 13,128
distinct properties associated with instances of dbo:Person and the
probability that a given property is found in an instance. It also
provides a list 438 other types that are declared in dbo:Person
instances which can be equivalents classes, superclasses, subclasses,
etc. http://tinyurl.com/dbo-person
The property explorer provides a list of 60347 properties with the
number of triples, number properties in each namespace etc. It also
allows searching. http://tinyurl.com/dbpedia-properties
Again, if we select a concrete property such as dbprop:name, it looks at
all the triples that contain the given property and analyze the subjects
and objects of those triples. For subjects, it looks at IRI / blank node
counts and also the their types. For objects, it does the same but
additionally analyzes literals for numeric, integers, averages, min,
max, etc. http://tinyurl.com/dbp-name
The triple pattern explorer allows you to search the 3,807,196 abstract
triple patterns. http://tinyurl.com/dbpedia-triple-patterns
Or you can select a pattern you are interested, for instance what are
the properties that connect dbo:Politician to dbo:Criminal
http://tinyurl.com/politician-criminal
In all these cases, the numbers are directly linked to the corresponding
triples.
That's a glimpse of Loupe. We would like to know whether it useful to
your use cases so that we can keep improving it. It's still in its early
stages so any feedback on improvements are more than welcome. If are
interested, we will we doing a demo [1] at ISWC 2015.
Best Regards,
Nandana Mihindukulasooriya
María Poveda Villalón
Raúl García Castro
Asunción Gómez Pérez
[1] Nandana Mihindukulasooriya, María Poveda Villalón, Raúl García
Castro, and Asunción Gómez Pérez. "Loupe - An Online Tool for Inspecting
Datasets in the Linked Data Cloud", Demo at The 14th International
Semantic Web Conference, Bethlehem, USA, 2015.
