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a query language was developed – SPARQL, which
can be used to “express queries across diverse data
sources, whether the data is stored natively as RDF or
viewed as RDF via middleware” (W3C, 2013).
The huge amount of such linked data be-
came one of the main showcases for successful
community-driven adoption of Semantic Web tech-
nologies (Berners-Lee et al., 2001). It aims at devel-
oping best practices to opening up the data thesaurus
on the Web, interlinking open data sets on the Web
and permitting Web developers to make use of that
rich source of information.
Additionally, the machine-understandable data
made available and the existing practices and tech-
nologies provide benefits for both end-users and en-
terprises. Knowledge bases have an important role in
enhancing the intelligence of Web and in supporting
information integration (DBpedia, 2013a).
DBpedia is one of the main projects of the se-
mantic Web, together with FOAF (Friend Of A
Friend) (Brickley and Miller, 2000), SIOC (Seman-
tically Interconnected Online Communities) (Breslin
and Bojars, 2004), GoPubMed (Doms and Schroeder,
2005) and NextBio (NextBio, 2013). It extracts struc-
tured information from Wikipedia and makes this in-
formation accessible on the Web under an open li-
cence terms. Wikipedia is an important source of in-
formation nowadays and many Web sites tend to pro-
vide information extracted from it, in a semantic and
meaningful way. We use DBpedia to provide a com-
mon knowledge model – expressed by a controlled
vocabulary and various well-known ontologies – and
a stabile service (endpoint), in order to give useful in-
formation to our users.
The data contained in DBpedia is quite impres-
sive. As stated on the Web site (DBpedia, 2013a),
the English version of the DBpedia knowledge base
currently describes 3.77 million things, out of which
2.35 million are classified in a consistent ontology, in
different categories like places, creative works, orga-
nizations, species, and many others. It provides lo-
calization in more than 100 languages and all these
versions together describe 20.8 million things, out of
which 10.5 million overlap (are interlinked) with con-
cepts from the English DBpedia. The full dataset con-
sists of 1.89 billion pieces of information stored as
RDF triples.
Unfortunately, there are few initiatives concern-
ing the querying and exploring such as big amount
of structured data with benefits for end-users or neo-
phytes. Thus, it is a real challenge to work with all
this data, to understand DBpedia inner structure and
to reuse it in personal future semantic Web work. In
most cases, the existing applications require various
knowledge regarding semantic Web key technologies.
These facts raised the idea of our Qsense project.
The DBpedia data set enables quite astonishing query
answering possibilities against Wikipedia data. There
is a public SPARQL endpoint over the DBpedia data
set (DBpedia, 2013b). The endpoint is provided using
Virtuoso (OpenLink, 2013) as the back-end database
engine.
For making all kind of queries against DBpedia,
we need a strong approach with respect to the Linked
Data principles (Bizer et al., 2009), which means
we need a method of publishing RDF data on the
Web and of interlinking data between different data
sources. Linked Data on the Web can be accessed us-
ing semantic Web browsers (or hyperdata software),
just as the traditional Web of documents is accessed
using traditional browsers. By using semantic Web
technologies, users could navigate, browse, and vi-
sualize – in an intelligent manner – different data
sources by following self-described RDF links. Ad-
ditionally, these technologies are used to model the
human knowledge as ontologies, to be further auto-
matically managed – stored, queried, filtered, trans-
formed, reused, etc. (Allemang and Hendler, 2011).
The information in Semantic Web is structured in
triples in the form of hsubject, predicate, objecti – the
RDF model. Altogether, the DBpedia dataset consists
of more than 100 million RDF triples and because of
this it is provided for download as a set of smaller
RDF files.
To understand and manually interrogate this large
amount of data is a very difficult task, especially for
the beginner students studying the semantic Web or
related fields.
3 REQUIREMENTS
AND FEATURES
In the Semantic Web community, a very important
feature is what someone is looking for and how the
RDF document is structured. Learning structured in-
formation reduces the amount of work that users may
have to perform. Also, people who have knowledge
of RDF want to perform specific queries by using
SPARQL language.
One possibility is to download a specific software,
like Twinkle (Dodds, 2007) – which needs Java to be
installed –, or to use a SPARQL endpoint – a public
Web service.
For such semantic Web developers, we are pre-
senting Qsense – a solution that is tailored to every
very specific use case, taking into account the limited
information about the term and the language users
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