=Paper=
{{Paper
|id=Vol-1193/paper_76
|storemode=property
|title=Graphol: Ontology Representation through Diagrams
|pdfUrl=https://ceur-ws.org/Vol-1193/paper_76.pdf
|volume=Vol-1193
|dblpUrl=https://dblp.org/rec/conf/dlog/ConsoleLSS14
}}
==Graphol: Ontology Representation through Diagrams==
https://ceur-ws.org/Vol-1193/paper_76.pdf
Graphol: Ontology Representation Through Diagrams
Marco Console, Domenico Lembo, Valerio Santarelli, and Domenico Fabio Savo
Dipartimento di Ing. Informatica, Automatica e Gestionale “Antonio Ruberti”
S APIENZA Università di Roma
Via Ariosto 25, I-00186 Roma, Italy
{console,lembo,santarelli,savo}@dis.uniroma1.it
Abstract. In this paper we present Graphol, a novel language for the diagram-
matic representation of Description Logic (DL) ontologies. Graphol is designed
with the aim of offering a completely visual representation to the users (notably,
no formulas need to be used in the diagrams), thus helping the understanding of
people not skilled in logic. At the same time, it provides designers with simple
mechanisms for ontology editing, which free them from having to write down
complex textual syntax. Through Graphol we can specify SROIQ(D) ontolo-
gies, thus our language essentially captures the OWL 2 standard. In this respect,
we developed a basic software tool to translate Graphol ontologies realized with
the yEd graph editor into OWL 2 functional syntax specifications. We conducted
some initial user evaluation tests, involving designers skilled in conceptual or on-
tology modeling and users without specific logic background. From these tests,
we obtained promising results about the effectiveness of our language for both
visualization and editing of ontologies.
1 Introduction
Ontologies are widely recognized as the best means to share domain knowledge in a
formal way. This implies that the representation they provide has to be agreed upon by
all their users, so that ontologies can act as reference models across groups of people,
communities, institutions, and applications. The relevance of this problem is also ampli-
fied by the increased uptake of ontologies in several contexts, such as biomedicine, life
sciences, e-commerce, cultural heritage, or enterprize applications [25]. Obviously, it is
very likely that people operating in such contexts are not experts in logic and generally
do not possess the necessary skills to interpret formulas through which ontologies are
typically expressed. This turns out to be a serious problem also (and probably mainly) in
the development of an ontology. Indeed, ontologists usually work together with domain
experts, the first providing their knowledge about ontology modeling and languages,
the latter providing their expertise on the domain of interest. During this phase, com-
munication between these actors is fundamental for the ontology to eventually become
a formal specification which faithfully represents the domain requirements.
We also notice that, despite the recent popularity of ontologies, there is still a seri-
ous lack of designers with the right skills for their modeling. In this respect, analysts
and experts in conceptual modeling for software and database design might be good
candidates to compensate for this lack, provided that they are trained to acquire the
needed knowhow. However, they need the right tools to approach the problem, possibly
close in spirit to those they usually adopt for conceptual modeling.
In the past years, some efforts have been made in this direction, and graphical syn-
taxes for ontologies based on the use of standard conceptual modeling languages have
been devised. In particular, UML profiles for OWL DL have been proposed in [8,16,24],
with the aim of making off-the-shelf UML tools usable for the purpose of ontology de-
velopment. These profiles have been devised for OWL 1 [7], but did not evolve there-
after towards OWL 2 [5], and extending them to all new features of the current standard
is not straightforward. An UML-based language for graphical editing of OWL 2 on-
tologies is instead at the basis of the more recent OWLGrEd tool [6,9]. In OWLGrEd,
however, many complex OWL expressions are specified through formulas in Manch-
ester syntax [19]. This somehow affects the graphical nature of the representation, espe-
cially for complex ontologies, where the proliferation of such formulas can compromise
intuitive understanding of the final ontology. The use of (variants of) UML for ontol-
ogy representation is also proposed in [17,18]. The first work, however, is focused on
foundational conceptual modeling languages, rather than on DL ontologies. Instead,
the tool described in [17] supports standard UML class diagrams (and also Entity-
Relationship ones), where complex definitions of ontology classes and roles must be
expressed through, e.g., OWL views. Thus, it does not provide extensions of standard
conceptual modeling languages to capture OWL. Another graphical notation for OWL,
not based on UML, is that adopted by the GrOWL ontology editor [22], which mod-
els ontologies as labeled graphs. Notably, the representation in GrOWL is completely
graphical, even though it relies on a quite large set of symbols and makes use of various
kinds of labeled edges. Also in this case the proposal is tailored to OWL 1, and it seems
that the development of the tool has been discontinued to date. We further notice that
other several formalisms for the graphical representation of knowledge have been pro-
posed during the years (see, e.g., [10,14,1]). Such approaches are either not tailored to
DLs or limited in their expressive power, i.e., they do not capture the OWL 2 standard,
or large fragments thereof. We finally point out that, besides the attempts to provide lan-
guages for the graphical specification of ontologies, much attention has been dedicated
to the issue of ontology visualization and various tools for it have been proposed, such
as the popular Protègè plugins OntoGraf1 and OWLViz2 . The aim of such solutions is to
ease navigation of the ontology, and thus typically they allow the user to obtain various
possible views of it, according to some graphical format. Such formats are therefore not
thought also for the editing task (see [21] for a survey on this matter).
In this paper we present our proposal for the graphical representation of DL ontolo-
gies and introduce the novel Graphol language. The main characteristics of Graphol
can be summarized as follows:
– Similarly to previous UML-based approaches, it is rooted in a standard language
for conceptual modeling. Indeed the basic components of Graphol are taken from
the Entity-Relationship (ER) model. Notably, simple ontologies that correspond to
classical ER diagrams (e.g., some ontologies specified in OWL 2 QL [23]) have in
Graphol a representation that is isomorphic to the ER one;
– Similarly to OWLGrEd, it is tailored to OWL 2, and thus it overcomes the limits of
previous UML profiles for OWL 1 and of other graphical notations for ontologies
1
http://protegewiki.stanford.edu/wiki/OntoGraf
2
http://protegewiki.stanford.edu/wiki/OWLViz
that capture less expressive DLs. More precisely, Graphol subsumes SROIQ(D),
the logical underpinning of OWL 2 [13,20]. Differently from OWLGrEd, however,
it offers a completely visual representation to the users (notably, no formulas need
to be used in Graphol diagrams);
– As in GrOWL, which as said is limited to OWL 1, a Graphol ontology is a graph,
but to draw it we resort to some standard symbols for denoting roles, concepts, and
attributes (those of the ER model), and do not use labeled edges. More precisely,
graph nodes represent either predicates from the ontology alphabet or constructors
used to build complex expressions from named predicates. Then, two kinds of edges
are adopted: Input edges, used to specify arguments of constructors, and inclusion
edges, used to denote inclusion axioms between (complex) expressions.
– Graphol has a precise syntax. The structure of its graphical assertions traces that
of typical DL axioms, so that our language has a natural encoding in DL. Through
such encoding, we assign our language with a clear and formal semantics. Syntax
and semantics of Graphol are presented in Section 2.
To support the graphical modeling of ontologies and at the same time foster the in-
teroperation with standard OWL reasoners and development environments, we have de-
vised a process that leads the designer from the drawing of a Graphol diagram, through
the use of the open source graph editor yEd3 , to the production of the corresponding
OWL 2 specification. We discuss these aspects in Section 3.
Finally, to verify the effectiveness of our language, we have conducted some user
evaluation tests, where both designers skilled in conceptual or ontology modeling and
users without specific logic background were involved. Our tests, though preliminary,
showed that Graphol has been perceived as a valuable and easy to use tool for both
ontology editing and visualization, and confirmed the importance for a visual ontol-
ogy language to be completely graphical, which is one of the distinguishing feature
of Graphol. A synthesis of our tests is given in Section 4, whereas for a complete de-
scription of our evaluation study, we refer the reader to the full version of the present
paper [12].
2 The Graphol language
Graphol syntax. The terms that make up the alphabet of the ontology, meaning the
named concepts, roles, attributes, value-domains (i.e., predetermined datatypes), and
constants (both individuals and values) are modeled by labeled rectangles, diamonds,
circles, rounded corner rectangles, and octagons, respectively. These symbols are called
predicate nodes, and the associated labels refer to predicates in the ontology alphabet.
We reserve two special labels, “Top” and “Bottom”, to represent, respectively,
the universal concept, value-domain, role, or attribute, and the empty concept, value-
domain, role, or attribute. For instance, a rectangle labeled “Top” represents the uni-
versal concept (>C ), while a rectangle labeled “Bottom” represents the empty concept
(⊥C ). Similarly for roles, attributes, and value-domains.
Other types of nodes, called constructor nodes, correspond to logical operators such
as intersection, union, negation (i.e., complement of), or restrictions on the domain and
3
http://www.yworks.com/en/products_yed_about.html
Symbol Name Symbol Name Symbol Name
Concept node Role node Attribute node
Restriction type
Value-domain Individual/ Domain restriction
node Value node node
Restriction type
Range restriction
and Intersection node or Union node
node
inv Inverse node oneOf One-of node not Complement node
chain Chain node
Symbol Name Symbol Name
Inclusion edge Input edge
Fig. 1: Graphol predicate and constructor nodes.
range of roles and attributes, and are used to graphically construct concept, role, at-
tribute, or value-domain expressions. Graphol nodes are shown in the top part of Fig-
ure 1, where the “Restriction type” label of the domain and range restriction nodes can
be equal to: “exists”, “forall”, “(x, −)”, or “(−, x)”, where x is a positive integer.
In Graphol, an edge can be of two types: input edge or inclusion edge. The former
is a dashed directed diamond edge, where the diamond indicates the target end, and
is used to construct complex expressions in the ontology by linking constructor and
predicate nodes. The latter is a solid directed arrow edge, where the arrow indicates
the target end, and is used to represent an inclusion assertion between expressions. The
input edge and the inclusion edge are depicted in the bottom part of Figure 1.
A Graphol expression is a weakly-connected acyclic directed graph, whose nodes
are both predicate and constructor nodes and whose edges are only input edges, that has
exactly one node with no outgoing edges. We call such node the sink of the expression.
We recall that a directed graph is called weakly-connected if replacing all of its directed
edges with undirected edges produces a connected (undirected) graph. The definition of
expressions in our language is formalized below. In such definition, when we mention
constructor nodes with input expressions we mean a (portion of a) graph where an input
edge goes from the sink of the expression to the constructor node.
Definition 1. A Graphol expression is built through one of the following specifications.
1. A concept expression is a weakly-connected acyclic graph, defined as follows.
– a concept node is a concept expression, whose sink node is the concept itself;
– a domain or range restriction node, labeled “exists”, “forall”, “(x, −)”, or
“(−, x)”, with one input role expression and one input concept expression, is
a concept expression, whose sink node is the domain or range restriction node;
– a domain restriction node, labeled “exists”, “forall”, “(x, −)”, or “(−, x)”,
with one input attribute expression and one input value-domain expression, is
a concept expression, whose sink node is the domain restriction node;
– an intersection or union node with n ≥ 2 input concept expressions is a concept
expression, whose sink node is the intersection node or the union node;
– a complement node with one input concept expression is a concept expression,
whose sink node is the complement node;
– a one-of node with n ≥ 1 input individual nodes is a concept expression, whose
sink node is the one-of node.
2. A role expression is a weakly-connected acyclic graph, defined as follows.
– a role node is a role expression, whose sink node is the role itself;
– an intersection or union node with n ≥ 2 input role expressions is a role ex-
pression, whose sink node is the intersection or the union node;
– an inverse node with one input role expression is a role expression, whose sink
node is the inverse node;
– a complement node with one input role expression is a role expression, whose
sink node is the complement node;
– a chain node with n ≥ 2 input role expressions, each one labeled with i such
that 1 ≤ i ≤ n and i 6= j for any two labels i and j, is a role expression, whose
sink node is the chain node.
3. An attribute expression is a weakly-connected acyclic graph, defined as follows.
– an attribute node is an attribute expression, whose sink node is the attribute
itself;
– an intersection or union node with n ≥ 2 input attribute expressions is an
attribute expression, whose sink node is the intersection or the union node;
– a complement node with one input attribute expression is an attribute expres-
sion, whose sink node is the complement node.
4. A value-domain expression is a weakly-connected acyclic graph, defined as follows.
– a value-domain node is a value-domain expression, whose sink node is the
value-domain node itself;
– a range restriction node, labeled “exists”, with one input attribute expression
is a value-domain expression, whose sink node is the range restriction node;
– an intersection or union node with n ≥ 2 input value-domain expressions is
a value-domain expression, whose sink node is the intersection or the union
node;
– a complement node with one input value-domain expression is a value-domain
expression, whose sink node is the complement node;
– a one-of node with n ≥ 1 input value nodes is a value-domain expression,
whose sink node is the one-of node.
As usual in ontology languages and DLs, intentional axioms are specified through
inclusions. In Graphol, an inclusion assertion is expressed through an inclusion edge
going from the (sink node of the) subsumed expression to the (sink node of the) sub-
sumer. In detail, a concept (resp., role, attribute, value-domain) inclusion assertion is
obtained by linking two sink nodes of concept (resp., role, attribute, value-domain)
expressions. Inclusion edges between expressions of different types (e.g., a concept ex-
pression with a role expression) are not allowed. Finally, we define a Graphol ontology
as a set of Graphol assertions.
Graphol semantics. We start with Graphol expressions and give their semantics by
providing their representation in terms of DL expressions, which in turn have a for-
mal semantics [4]. To this aim, we define a function Φ that takes as input a Graphol
expression EG and returns a DL expression that represents it.
In formalizing Φ, we denote with sink(EG ) the sink node of EG , and with arg(EG )
the possibly empty set of Graphol expressions that are linked to sink(EG ) by means of
input edges. Then, we define Φ as follows:
– if sink(EG ) is a concept, role, attribute, value-domain, or individual/value node
labeled S, then Φ(EG ) = S 4 ;
– if sink(EG ) is a domain restriction node labeled “exists” (resp., “forall”, “(x, −)”,
“(−, x)”), and arg(EG ) = {RA , CV }, then Φ(EG ) = ∃Φ(RA ).Φ(CV ) (resp.,
Φ(EG ) = ∀Φ(RA ).Φ(CV ), Φ(EG ) =≥ x Φ(RA ).Φ(CV ), Φ(EG ) =≤
y Φ(RA ).Φ(CV ));
– if sink(EG ) is a range restriction node labeled “exists” (resp. “forall”, “(x, −)”,
“(−, x)”), and arg(EG ) = {R , C }, then Φ(EG ) = ∃(Φ(R ))− .Φ(C ) (resp.
Φ(EG ) = ∀(Φ(R ))− .Φ(C ), Φ(EG ) =≥ x (Φ(R ))− .Φ(C ), Φ(EG ) =≤
y (Φ(R ))− .Φ(C ));
– if sink(EG ) is a range restriction node labeled “exists” and arg(EG ) = {A }, then
Φ(EG ) = ∃(Φ(A ))− ;
– if sink(EG ) is a complement node and arg(EG ) = {}, then Φ(EG ) = ¬Φ();
Snsink(E
– if
i
G ) is an intersection
dn (resp.i a union or a one-of Fnode) and arg(EG ) =
n i
i=1 , then Φ(E G ) = i=1 Φ( ) (resp. Φ(E G ) = i=1 Φ( ), Φ(EG ) =
1 n
{Φ( ), . . . , Φ( )});
– if sink(EG ) is an inverse node and arg(EG ) = {R }, then Φ(EG ) = (Φ(R ))− ;
– if sink(EG ) is a chain node and arg(EG ) = {1R , . . . nR }, where each iR , with
1 ≥ i ≥ n, is a Graphol role expression linked to sink(EG ) by an input edge
labeled i, then Φ(EG ) = Φ(1R ) ◦ Φ(2R ) ◦ · · · ◦ Φ(nR ).
Similarly, in order to provide the semantics of Graphol inclusion assertions, we
define a function Θ that takes as input one such inclusion AG and returns its DL repre-
sentation. We denote with source(AG ) the Graphol expression whose sink node is the
source of the inclusion edge in AG , and with target(AG ) the Graphol expression whose
sink node is the target of the inclusion edge in AG . The function Θ is thus defined as
Θ(AG ) = Φ(source(AG )) v Φ(target(AG )). Given a Graphol ontology OG , we are
able to translate it into a DL ontology ODL by applying Θ to each assertion in OG . The
semantics of OG coincides with the semantics of ODL , for which we refer to [4].
In Tables 1 and 2 we provide an example of the application of the function Φ to
Graphol expressions of “depth” 0 or 1, i.e., expressions formed only by predicate nodes
or by a constructor node taking as input only predicate nodes.
It is easy to see that any DL ontology produced by the function Θ subsumes a
SROIQ(D) ontology. Furthermore, the inverse of the functions Φ and Θ can be de-
fined analogously, thus showing that SROIQ(D) is indeed subsumed by Graphol.
4
We assume the alphabets for concepts, roles, attributes, value-domains, individuals, and values
to be pairwise disjoint, so that it is clear which kind of predicate S represents. Also, with a little
abuse of notation, if S =“Top” (resp. S =“Bottom”), we assume the Φ returns the correct DL
universal (resp. empty) predicate, depending on whether sink(EG ) is a concept, role, attribute,
or value-domain.
Expression DL-syntax Graphol syntax
Atomic Concept A
Domain restriction ∃R.C ∀R.C
on role ≥ xR.C ≤ xR.C
Range restriction ∃R− .C ∀R− .C
on role ≥ xR− .C ≤ xR− .C
Domain restriction ∃U.V ∀U.V
on attribute ≥ xU.V ≤ xU.V
Concept Intersection C uD
Concept Union C tD
Concept Complement ¬C
Concept One-of {a, b, c}
Atomic Role R
Role Intersection QuR
Role Union QtR
Role Inverse R−
Role Complement ¬R
Role Chain Q◦R
Table 1: DL-Graphol correspondence for concept and role expressions of depth 0 or 1.
Expression DL-syntax Graphol syntax
Attribute U
Attribute Intersection U1 u U2
Attribute Union U1 t U2
Attribute Complement ¬U1
Value-domain V
Range existential
∃U −
restriction on attribute
Value-domain
V1 u V2
Intersection
V1 t V2
Value-domain Union
Value-domain
¬V
Complement
Value-domain One-of {1, 2, 3}
Table 2: DL-Graphol correspondence for attribute and value-domain expressions of depth 0 or 1.
Shortcuts. In order to aid the user in the design of a Graphol ontology, we have de-
fined some shortcuts that allow for a more compact representation of frequently used
expressions and assertions. Here we introduce two of them.
The first shortcut is the disjoint union node, which is shaped as a black hexagon.
This special node is used to represent a union expression, and at the same time it im-
poses the disjointness between its arguments. Therefore, it also represents negative in-
clusion assertions involving its arguments. An example of its use is given in Figure 2.
Fig. 2: Example of a disjoint concept hierarchy represented in Graphol with (left-hand side figure)
and without (right-hand side figure) the disjoint union node.
We also define a new edge, called global functionality edge. which is an input edge
with an additional vertical line on its source end. It is used to define the functionality of
a role (resp. an inverse role), by linking it to a domain (resp. range) “exists” restriction
node. Similarly for the functionality of an attribute. An example is given in Figure 3.
Fig. 3: Example of Graphol global functionality edge: from left to right we specify role function-
ality, inverse role functionality, and attribute functionality.
Example. In Figure 4, we provide the Graphol specification of a portion of the Pizza
ontology. We also present below a logically equivalent representation of such ontology
given in terms of DL assertions.
CheeseT opping v P izzaT opping F ood v ∃calories
CheeseT opping v ¬M eatT opping P izza v F ood
M eatT opping v P izzaT opping P izza v ¬P izzaT opping
M eatT opping v ¬CheeseT opping CheeseyP izza v P izza
P izzaT opping v F ood M eatyP izza v P izza
P izzaT opping v ¬P izza M eatyP izza v ¬V egetarianP izza
V egetarianP izza v P izza ∃hasIngredient v F ood
∃hasIngredient− v F ood hasT opping v hasIngredient
∃hasT opping v F ood ∃hasT opping v P izzaT opping
∃calories v F ood ∃calories− v xsd : integer
V egetarianP izza ≡ ¬hasT opping.M eatT opping
Fig. 4: Graphol ontology example: an excerpt of the Pizza ontology.
3 Tools supporting Graphol
As a must-have for Graphol, we singled out the importance of having an automated
translation tool to produce OWL 2 specifications from Graphol ones that are compatible
with this standard. We also tried to provide this new language with an easy way to
design, edit, and debug the graphical specifications. To these aims, we devised a process
for ontology designers that relies on both existing open source tools and originally
developed software components.
The process starts specifying, by means of the yEd editor, a Graphol diagram, en-
coded in the graphml format5 . The use of a third party software provides the developers
with a large set of features, professionally devised for supporting the editing of dia-
grams of all sorts. To further support editors, we packed up a yEd palette containing all
and only the symbols needed for editing a Graphol ontology. This comes in handy in
trying to overcome the lack of specificity of the editor.
We then implemented a tool capable of translating a set of yEd diagrams specify-
ing a Graphol ontology into an equivalent OWL 2 encoding, if any. More precisely, the
tool is devised to be both a support for the editing tasks and an automatic translator
that produces an OWL 2 functional syntax specification. The latter is achieved by pars-
ing the yEd files and translating each portion with the corresponding OWL statement,
straightforwardly applying the rules sketched in Section 2. As for the editing aids, the
tool provides a syntactic validation of a given diagram. While parsing, if a portion of the
graph is found such that either it is non well-formed, i.e., it does not respect the Graphol
syntax, or does not correspond to any OWL 2 statement, the process terminates. If the
translation was not successful, the tool shows, by means of an external yEd viewer, one
of the nodes in the identified untranslatable portion and describes the recognized error
in a pop-up window. Both the translator tool and the yEd palette for Graphol can be
downloaded from the web site http://www.dis.uniroma1.it/˜graphol/.
4 User Evaluation
In this section we briefly discuss the user evaluation tests we have conducted for the
Graphol language. The goal of these tests is to evaluate the effectiveness of Graphol
5
http://graphml.graphdrawing.org/index.html
for ontology comprehension and design by users with different backgrounds and vary-
ing levels of expertise. To achieve this goal, we have conducted two separate studies,
designed around a series of model comprehension and model editing tasks which were
performed individually by the participants.
– The first is a comparative study in which participants were asked to perform two
series of eight comprehension tasks on two ontologies modeled respectively in
Graphol and in a different graphical ontology language. For this study we chose
a group of ten computer science Ph.D. and Master’s students with only basic skills
in conceptual modeling, and whose experience with ontologies (if any) is limited
to working with ontologies defined by others. We refer to this group as ontology
consumers.
– The second is a non-comparative user study in which participants were asked to
answer ten questions and to perform ten editing tasks on two Graphol ontologies.
For this study we selected a group of twelve computer science Ph.D. students, post-
docs, and researchers with advanced knowledge in conceptual modeling and some
basic skills in logic (but only a small subset of them with some experience in on-
tology design). We refer to this group as ontology designers.
We point out that the number of participants to both our evaluation study is in line
with guidelines on usability testing [15].
The language we chose for the comparative study was OWLGrEd [6,9]. OWLGrEd
was chosen because its UML-based design principles and visual representation are quite
different from Graphol, but are, at least in principle, easily accessible to users who are
familiar with UML class diagrams. We point out that this requirement was fulfilled by
all participants to this test, though at different expertise levels, and that such participants
only had limited knowledge on ontology languages (or none).
We avoided extending the second study with comparative test between Graphol and
OWLGrEd for ontology editing purposes because this test would have been strongly
influenced by the tools used for the task, and an evaluation of these tools was outside the
scope of the experiments (in particular because OWLGrEd comes with its own editor
while Graphol currently does not). Furthermore, on a more practical note, this would
have excessively drawn out the duration of the test (as it is, it exceeded two hours). We
also did not conduct a comparative evaluation of Graphol against ontology editors such
as Protègè, or simply against modeling through DL axioms, because these approaches
obviously do not offer graphical editing solutions, whereas our main aim has been to
test the evaluation of consumers and designers not necessary skilled in ontologies.
In Figure 5 we provide some of the results we obtained through our tests. From
left to right, the figure shows the average results in terms of correctness (percentage of
correct answers) for the comprehension tasks in Graphol and OWLGrEd conducted by
ontology consumers, the average correctness results for the comprehension and editing
tasks in Graphol conducted by ontology designers, and the overall average times for the
Graphol comprehension and editing tasks (in minutes, where 2.5 minutes for compre-
hension questions and 4 minutes for editing questions were the predetermined bench-
mark values). Clearly, the very high average correctness scores and low average times
produced by the ontology designers in both tasks show an excellent comprehension
of the Graphol language by these users, and a good ability in performing the required
Ontology Consumers: Average Correctness Ontology Designers: Average Correctness Average Times
100,0% 100,0% 4 3,8
84% 3,5
74,50% 74% 75%
75,0% 75,0% 3
2,51
2,5
50,0% 50,0% 2
1,5
25,0% 25,0% 1
0,5
0,0% 0,0% 0
Graphol Comprehension OWLGrEd Comprehension Graphol Comprehension Graphol Editing Graphol Comprehension Graphol Editing
Fig. 5: Average results for percentage of correct answers and for overall times.
Graphol ontology modeling tasks. Furthermore, the ontology consumers, who did not
have advanced knowledge on conceptual modeling, nevertheless showed a good level of
comprehension of the Graphol language, comparable to, and in some cases better than,
the one showed for OWLGrEd, which is strongly based on a formalism which they were
familiar with. According to our results and to the feedback we obtained by users from
several open questions that were presented in a post-test questionnaire and from a brief
discussion that concluded the tests, we also observed that Graphol has been perceived
as easier than OWLGrEd for more complex expressions, which in OWLGrED require
the use of Manchester syntax formulas.
5 Conclusion
Besides the user evaluation tests we have described in Section 4, we could assess the
effectiveness of Graphol also through its use in some industrial projects in which we
have been recently involved (see, e.g., [2,3]). In these projects, we developed quite
large ontologies, comprising hundreds of predicates and axioms, and have been in touch
with domain experts without any background on ontologies or formal languages. This
has also been a valuable test bed for the design process we have described in Sec-
tion 3, which allowed us to automatically obtain processable OWL encodings from our
Graphol specification, which instead turned out to be crucial for communication with
domain experts.
Both user evaluation tests and practical experience however have highlighted the
need of a dedicated editing tool specifically built to support the Graphol ontology spec-
ification. The development of such tool is our main future work on Graphol. At the same
time, we are working to improve Graphol visualization. A first step on this direction has
been the use of a specific component for visualizing Graphol ontologies in Mastro Stu-
dio [11], a tool for the management of ontology-based data access applications. In this
tool, Graphol ontologies are suitable connected to wiki-like ontology documentation,
through hyperlinks from the elements of the diagrams to wiki pages associated to such
elements. For the future, we plan to enrich such features, e.g., by enabling construction
of on the fly Graphol excerpts of the ontology, based on specific user requests.
Acknowledgments. This research has been partially supported by the EU under FP7
project Optique (grant n. FP7-318338).
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