=Paper=
{{Paper
|id=Vol-2187/paper6
|storemode=property
|title=Sparqling: Painlessly Drawing SPARQL Queries over Graphol Ontologies
|pdfUrl=https://ceur-ws.org/Vol-2187/paper6.pdf
|volume=Vol-2187
|authors=Sara Di Bartolomeo,Gianluca Pepe,Domenico Fabio Savo,Valerio Santarelli
|dblpUrl=https://dblp.org/rec/conf/semweb/BartolomeoPSS18
}}
==Sparqling: Painlessly Drawing SPARQL Queries over Graphol Ontologies==
https://ceur-ws.org/Vol-2187/paper6.pdf
S PARQLING: painlessly drawing SPARQL queries over
G RAPHOL ontologies
Sara Di Bartolomeob , Gianluca Pepeb ,
Valerio Santarellia,b , Domenico Fabio Savoa,b
(a) Sapienza Università di Roma
hlastnamei@diag.uniroma1.it
(b) OBDA Systems
hlastnamei@obdasystems.com
Abstract. There is a reason why the proverb says “A picture is worth a thousand
words”: diagrams or sketches of concepts and of the relationships that exist be-
tween them help understand their meaning. In the Semantic Web, the concepts
and relationships of the world are modeled through ontologies, but current stan-
dards for ontology languages such as OWL 2 don’t buy into this idea, recur-
ring to formula-based representations, that work well with machines and not so
well with humans. Similarly, the languages used to query ontologies, in particu-
lar SPARQL, are not immediately intuitive for people who lack specific training
or background, so providing graphical support for query formulation can make
the life of the end user easier. In this work we present the S PARQLING system
for SPARQL query building based on the G RAPHOL visual language for ontolo-
gies. The characterizing feature of S PARQLING is the idea to preserve and take
advantage of the native diagrammatic representation of G RAPHOL ontologies, al-
lowing the user to navigate it, and to construct a graph-based representation of
the query over it through a simple point-and-click mechanism. The system then
automatically transforms the graphical query into the SPARQL syntax.
1 Introduction
Ontologies provide a conceptualization of a domain of interest, meant to foster knowl-
edge sharing and integration among different information systems, and to support hu-
man understanding and communication through a high level of abstraction that is clear
for domain experts. Recent years have seen a sharp increase in the popularity of on-
tologies in modern applications, underlined by wide-spread use in biomedicine, e-
commerce, Semantic Web, and Data Integration [23].
Ontologies can be formalized in different languages, from Description Logics [4]
(DLs) to RDF(S)1 to OWL 22 , the latter two being W3C standards, and the use of
these languages is propaedeutic to enabling automated reasoning techniques over on-
tologies in information systems. However, people operating in organizational settings
typically do not possess the necessary skills to interpret the formulas that are used as
1
http://www.w3.org/TR/rdf-primer-20040210/
2
http://www.w3.org/TR/owl2-primer
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Sparqling: painlessly drawing SPARQL queries over Graphol ontologies
means of expression in such languages. This introduces a bottleneck in the ontology
design phase, where ontology engineers usually work together with domain experts. To
alleviate this issue, there have been various efforts in recent years to devise graphical
ontology syntaxes based on standard conceptual modeling languages, e.g., UML class
diagrams or ER diagrams [6,10,11,5]. These languages however commonly suffer from
a lack of expressiveness, being unable to represent OWL 2 ontologies in a completely
graphical way. In some cases, a solution has been to present a language that introduces
formulaic elements, which is a choice that hinders the diagrammatic representation of
the ontology and its intuitive understanding. Similar difficulties arise, particularly in
the enterprise context, with other visual ontology notations that are not based on lan-
guages such as UML and ER [15]. The latest contribution to this line of research is
the G RAPHOL language [9,16]. G RAPHOL resembles ER and UML class diagrams, but
has an inherent formal semantics based on DLs, presents a entirely graphical syntax,
and is able to fully capture OWL 2. The effectiveness of G RAPHOL as the language for
representing ontologies has been proven in various research and industrial projects in
recent years [2,3]. G RAPHOL comes equipped with its own custom editor, Eddy [17],
which allows to draw ontologies in G RAPHOL, and export them into OWL 2.
Aside from acting as a standardized reference model, ontologies can also be used as
a tool for accessing and managing data. In this scenario, the use of triple stores for man-
aging (Linked) enterprise data is quickly spreading [24], and semantic technologies,
particularly ontologies, are adopted as a means to manage legacy data sources [18]. The
standard language for querying both triples stores and ontologies is SPARQL3 . This lan-
guage, while somewhat simpler in structure than traditional structured query languages
such as SQL, is still however not immediately intuitive for people who lack specific
training or background, and thus formulating SPARQL queries can be quite challeng-
ing for end users. Once again, graphical support could be an effective solution for query
formulation. Indeed, such solutions have been the subject of research for many years
now [8], going back to the development of visual query systems and languages (VQSs
and VQLs) for structured and semi-structured data, which utilize techniques such as di-
rect manipulation of visual objects that represent the domain and query language. These
early approaches [7,8] established the research field, but their success remains some-
what limited in practice. More recently, various solutions for SPARQL query building
systems have been proposed, most commonly recurring to some sort of graphical repre-
sentation or interface to construct the query and to exploiting the axioms in the ontology
to suggest relevant parts of it. These works can be divided into two broad categories.
The first are approaches that attempt to exploit the alphabet of the ontology to formulate
queries in natural language, or to iteratively build them through context-sensitive com-
pletion mechanisms, and then produce the corresponding SPARQL query [12,14,19].
The second, instead, adopt a graphical notation, most typically graph-based, for the
query, and provide GUI-support for its construction [1,13,21,20,22,25].
In this work we present the S PARQLING system for SPARQL query building based
on the G RAPHOL visual language for ontologies. The characterizing feature of S PAR -
QLING , which differentiates it from these previous works, is the idea to preserve and
take advantage of the native diagrammatic representation of G RAPHOL ontologies, al-
3
https://www.w3.org/TR/rdf-sparql-query/
65
Sparqling: painlessly drawing SPARQL queries over Graphol ontologies
Fig. 1. G RAPHOL representation of a portion of the Base Registry of Individual, Families, and
Cohabitation ontology, developed for the Italian National Institute of Statistics.
lowing the user to navigate it, and to construct a graph-based representation of the query
over it through a simple point-and-click mechanism. The system then automatically
transforms the graphical query into the SPARQL syntax. S PARQLING is an open-source
project, available on Github4 , and is supported by OBDA Systems5 .
In the following sections we first provide some background on the G RAPHOL lan-
guage and on SPARQL, and then we introduce the S PARQLING system. Finally, we
conclude the work with a brief discussion on future developments of the system.
2 Background
In this section, we briefly introduce the G RAPHOL [9,16] visual language for OWL 2
ontologies and the SPARQL query language.
G RAPHOL is a completely graphical language for representing OWL 2 ontologies
which has an inherent formal semantics based on DLs and resembles ER diagrams,
but which is in fact able to fully capture OWL 2 without recurring to formulas or an-
notations. The basic idea behind G RAPHOL is to represent the ontology as a set of
inclusions, as in DLs or OWL 2, while preserving a graphical representation of it, given
in terms of a directed graph. The nodes of the graph are atomic predicates or graphi-
cal operators, whereas the edges are solid or dashed arrows. The latter denote input to
operators, whereas the former denote inclusions. In G RAPHOL, rectangles are used for
classes (called entities in ER), diamonds are used for object properties (corresponding
to binary relationships in ER), circles are used for data properties, and rounded rectan-
gles are used for data types (e.g., string and integer). G RAPHOL expressions are
either nodes representing predicates of the ontology (e.g., named classes and proper-
ties), or a combination of these nodes with other nodes that represent operators, shaped
as blank or solid boxes, or hexagons. For instance, labeled blank and solid boxes are
used for constructing object or data property restrictions on the domain and range, re-
spectively, and solid hexagons represent disjoint unions. Figure 1 depicts a portion of
the Base Registry of Individual, Families, and Cohabitation ontology, developed for the
Italian National Institute of Statistics [3] in G RAPHOL.
4
https://github.com/picorana/painless_sparql, where a demo is available.
5
https://www.obdasystems.com/
66
Sparqling: painlessly drawing SPARQL queries over Graphol ontologies
Fig. 2. The user interface of the S PARQLING system.
SPARQL is the W3C recommendation query language for RDF. Its structure is
based on graph patterns and query evaluation consists in subgraph matching. The ba-
sic building block of a SPARQL query is a basic graph pattern (BGP), which is a set of
triple patters, i.e., RDF triples that may contain query variables at the subject, predicate,
and object position. More complex SPARQL query patterns are built over BGPs.
3 The System
S PARQLING is a web-based application for constructing SPARQL queries by exploit-
ing the G RAPHOL representation of the ontology. The user interface is split in three
different sections, as shown in Figure 2. On the left, the user can see the G RAPHOL
representation of the ontology, while on the bottom right the query canvas displays the
visual representation of the query under construction, and on the top right the system
provides the SPARQL translation of the query. The current implementation of S PAR -
QLING is tailored towards queries for Ontology-based Data Management systems [18],
for which the support of the conjunctive query fragment of SPARQL is sufficient.
The distinguishing feature of S PARQLING is that the system keeps the G RAPHOL
representation of the ontology always accessible to the user. The intuition is that having
a clear understanding of the structure and content of the ontology, in its G RAPHOL
form, is helpful in designing queries. Indeed, the mental process of writing a query is
natural when looking at a G RAPHOL diagram because it recalls the act of tracing a path
on it. S PARQLING attempts to capture these query paths on the diagram through the
visual representation of the query provided in the query canvas.
This representation, which we refer to as query graph, indeed has the structure of a
graph, to naturally recall SPARQL’s BGPs. Generally speaking, a triple pattern in the
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Sparqling: painlessly drawing SPARQL queries over Graphol ontologies
query corresponds to an edge connecting two nodes, each representing a variable or a
constant. To provide a visual correspondence with G RAPHOL, the graphical represen-
tation of the ontology predicates in the query graph is the same as in G RAPHOL.
To add a triple to the basic graph pattern of the query, the user double clicks on a
predicate symbol in the G RAPHOL diagram. According to the specific predicate symbol,
the corresponding edge and nodes are automatically added to the query graph. If a node
of the query graph is selected prior to choosing the symbol in the diagram, the new edge
will be linked to it. Otherwise, a new disconnected component will be added. Actions
on specific nodes of the graph are performed through contextual menus that appear by
right clicking on a node, allowing for renaming, deleting, switching between variable
and constants, adding to the select clause, and specifying filter expressions.
The system maintains at all times a SPARQL representation of the query which the
user can inspect and modify. Each modification of the SPARQL query is immediately
mirrored in the query graph, and viceversa. By hovering onto the name of a variable in
the SPARQL query, the corresponding node in the query graph is highlighted in order
to make its position stand out. The user can actually use S PARQLING by interacting
exclusively with the query graph or with the SPARQL representation, according to the
user’s preference. The application is developed in JavaScript, using the Cytoscape.js
library6 for the rendering of the G RAPHOL diagram and the query graph, and the cola.js
library7 for the force-directed layout of the query graph.
4 Conclusions
We have presented the S PARQLING system for drawing SPARQL queries over
G RAPHOL ontologies. The development of this system aims to allow users of SPARQL-
based query systems to produce queries in a more intuitive fashion. Although the current
version is fully functional, the S PARQLING project is still under development and will
be extended in different directions. We aim to extend the portion of SPARQL which is
currently supported by S PARQLING; we will work to further improve the synergy be-
tween the G RAPHOL diagram and the query graph that the user is constructing; and we
will allow importing SPARQL queries over the ontology into the system. Finally, test-
ing of the system has so far been carried out through its use in the field; we would like
to conduct a more structured and thorough user evaluation to identify potential areas
of improvement in the system. Moreover, we plan to conduct a comparative evaluation
with other graphical SPARQL editors.
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