=Paper=
{{Paper
|id=Vol-1704/paper1
|storemode=property
|title=Extending OWL Ontology Visualizations with Interactive Contextual Verbalization
|pdfUrl=https://ceur-ws.org/Vol-1704/paper1.pdf
|volume=Vol-1704
|authors=Uldis Bojārs,Renārs Liepiņš,Normunds Grūzītis,Kārlis Čerāns,Edgars Celms
|dblpUrl=https://dblp.org/rec/conf/semweb/BojarsLGCC16
}}
==Extending OWL Ontology Visualizations with Interactive Contextual Verbalization==
https://ceur-ws.org/Vol-1704/paper1.pdf
Extending OWL Ontology Visualizations with
Interactive Contextual Verbalization
Uldis Bojārs1,2 , Renārs Liepiņš1 , Normunds Grūzı̄tis1,2 , Kārlis Čerāns1,2 , and
Edgars Celms1,2
1
Institute of Mathematics and Computer Science, University of Latvia
Raina bulvaris 29, Riga, LV-1459, Latvia
2
Faculty of Computing, University of Latvia
Raina bulvaris 19, Riga, LV-1459, Latvia
uldis.bojars@lumii.lv, renars.liepins@lumii.lv, normunds.gruzitis@lumii.lv,
karlis.cerans@lumii.lv, edgars.celms@lumii.lv
Abstract. To participate in Semantic Web projects, domain experts
need to be able to understand the ontologies involved. Visual notations
can provide an overview of the ontology and help users to understand
the connections among entities. However, users first need to learn the
visual notation before they can interpret it correctly. Controlled natu-
ral language representation would be readable right away and might be
preferred in case of complex axioms, however, the structure of the on-
tology would remain less apparent. We propose to combine ontology vi-
sualizations with contextual ontology verbalizations of selected ontology
(diagram) elements, interactively displaying controlled natural language
(CNL) explanations of OWL axioms corresponding to the selected visual
notation elements. Thus, the domain experts will benefit from both the
high-level overview provided by the graphical notation and the detailed
textual explanations of particular elements in the diagram.
Keywords: OWL · Ontology visualization · Contextual verbalization ·
Controlled natural language
1 Introduction
Semantic Web technologies have been successfully applied in pilot projects and
are transitioning toward mainstream adoption in the industry. In order for this
transition to go successfully, there are still hurdles that have to be overcome.
One of them are the difficulties that domain experts have in understanding
mathematical formalisms and notations that are used in ontology engineering.
Visual notations have been proposed as a way to help domain experts to work
with ontologies. Indeed, when domain experts collaborate with ontology experts
in designing an ontology “they very quickly move to sketching 2D images to
communicate their thoughts” [8]. The use of diagrams has also been supported
by an empirical study done by Warren et al. where they reported that “one-third
[of participants] commented on the value of drawing a diagram” to understand
what is going on in the ontology [21].
5
Extending OWL Ontology Visualizations with Interactive Contextual Verbalization
Despite the apparent success of the graphical approaches, there is still a
fundamental problem with them. When a novice user wants to understand a
particular ontology, he or she cannot just look at the diagram and know what it
means. The user first needs to learn the syntax and semantics of the notation –
its mapping to the underlying formalism. This limitation has long been noticed
in software engineering [18] and, for this reason, formal models in software en-
gineering are often translated into informal textual documentation by systems
analysts, so that they can be validated by domain experts [4].
A similar idea of automatic conversion of ontologies into seemingly informal
controlled natural language (CNL) texts and presenting the texts to domain
experts has been investigated by multiple groups [14,20,9]. CNL is more under-
standable to domain experts and end-users than the alternative representations
because the notation itself does not have to be learned, or the learning time is
very short. Hoverer, the comparative studies of textual and graphical notations
have shown that while domain experts that are new to graphical notations better
understand the natural language text, they still prefer the graphical notations in
the long run [13,17]. It leads to a dilemma of how to introduce domain experts to
ontologies. The CNL representation shall be readable right away and might be
preferred in case of complex axioms (restrictions) while the graphical notation
makes the overall structure and the connections more comprehensible.
We present an approach that combines the benefits of both graphical nota-
tions and CNL verbalizations. The solution is to extend the graphical notation
with interactive contextual verbalizations of the axioms that are represented by
the selected graphical element. The graphical representation gives the users an
overview of the ontology while the contextual verbalizations can explain what
the particular graphical element means. Thus, domain experts that are novices
in ontology engineering shall be able to learn and use the graphical notation
rapidly and independently without special training.
Throughout this paper we refer to the OWLGrEd visual notation and ontol-
ogy editing and visualization tools that are using it. The OWLGrEd notation [1]
is a compact and complete UML-style notation for OWL 2 ontologies. It relies
on Manchester OWL Syntax [7] for certain class expressions. This notation is
implemented in the OWLGrEd ontology editor3 and its online ontology visual-
ization tool4 [11]. The approach proposed in this article is demonstrated on an
experimental instance of the OWLGrEd visualization tool.
In Section 2, we present the general principles of extending graphical ontol-
ogy notations with contextual natural language verbalizations. In Section 3, we
demonstrate how the proposed approach may be used in practice by extending
ontology visualizations in the OWLGrEd notation with interactive contextual
verbalizations in controlled English. In Section 4, we discuss the benefits and
limitations of our approach. Section 5 discusses related work while Section 6
summarizes the article.
3
http://owlgred.lumii.lv
4
http://owlgred.lumii.lv/online_visualization/
6
Extending OWL Ontology Visualizations with Interactive Contextual Verbalization
2 Extending Graphical Notations with Contextual
Verbalizations
This section describes the proposed approach for contextual verbalization of
graphical elements in ontology diagrams, starting with a motivating example. We
are focusing particularly on OWL ontologies, assuming that they have already
been created and that the ontology symbols (names) are lexically motivated and
consistent, i.e., in this paper we do not consider the authoring of ontologies,
although the contextual verbalizations might be helpful in the authoring process
as well and it would provide a motivation to follow a lexical and consistent
naming convention.
2.1 Motivating Example
In most diagrammatic OWL ontology notations, object property declarations
are shown either as boxes (e.g. in VOWL [12]) or as labeled links connecting the
property domain and range classes (e.g. in OWLGrEd [1] or Graffoo [3]). Fig-
ure 1 illustrates a simplified ontology fragment that includes classes Person and
Thing, an object property likes and a data property hasAge. This fragment is
represented by using three alternative formal notations: Manchester OWL Syn-
tax [7], VOWL and OWLGrEd. As can be seen, the visualizations are tiny and
may already seem self-explanatory. Nevertheless, even in this simple case, the
notation for domain experts may be far from obvious. For example, the Manch-
ester OWL Syntax uses the terms domain and range when defining a property,
and these terms may not be familiar to a domain expert. In the graphical nota-
tions, the situation is even worse because the user may not even suspect that the
edges represent more than one assertion and that the assertions are far-reaching.
In the case of likes in Figure 1, it means that everyone that likes something is
necessarily a person.
We have encountered such problems in practice when introducing ontologies
visualized using the OWLGrEd notation to users familiar with the UML nota-
tion. Initially, the users were misunderstanding the meaning of the association
edges. For example, they would interpret that the edge likes in Figure 1 means
“persons may like persons”, which is true, however, they would also assume that
other disjoint classes could have this property, which is false in OWL because
multiple domain/range axioms of the same property are combined to form an
intersection. Thus, even having a very simple ontology, there is a potential for
misunderstanding the meaning of both the formal textual notation (e.g., Manch-
ester OWL Syntax) and the graphical notations.
2.2 Proposed Approach
We propose to extend graphical ontology diagrams with contextual on-demand
verbalizations of OWL axioms related to the selected diagram elements, with
the goal to help users to better understand their ontologies and to learn the
graphical notations based on their own and/or real-world examples.
7
Extending OWL Ontology Visualizations with Interactive Contextual Verbalization
Class: Person
Manchester
SubClassOf: owl:Thing Every person is a thing.
Notation
ObjectProperty: likes
Language
Everything that likes something is a person.
Natural
Domain: Person
Range: Person Everything that is liked by something is a person.
DataProperty: hasAge Everything that has an age is a person.
Domain: Person Everything that is an age of something is an integer.
Range: xsd:integer
OWLGrEd
Notation
Notation
VOWL
Person hasAge integer
(external)
likes
Fig. 1. A simplified ontology fragment alternatively represented by using Manchester
OWL Syntax, VOWL and OWLGrEd, and an explanation in a controlled natural
language
The contextual verbalization of ontology diagrams relies on the assumption
that every diagram element represents a set of ontology axioms, i.e., the ontology
axioms are generally presented locally in the diagram, although possibly a single
ontology axiom can be related to several elements of the diagram.
The same verbalization can be applied to all the different OWL visual nota-
tions, i.e., we do not have to design a new verbalization (explanation) grammar
for each new visual notation, because they all are mapped to the same underlying
OWL axioms. Thus, the OWL visualizers can reuse the same OWL verbalizers
to provide contextual explanations of any graphical OWL notation.
By reusing ontology verbalizers, existing ontology visualization systems can
be easily extended with a verbalization service. Figure 2 illustrates how the
proposed approach might work in practice:
1. Visualizer is the existing visualization component that transforms an OWL
ontology into its graphical representation.
2. The system is extended by a User Selection mechanism that allows users to
select the graphical element that they want to verbalize.
3. Collector gathers a subset of the ontology axioms that correspond to the
selected graphical element.
4. The relevant axioms are passed to Verbalizer that produces CNL statements
– a textual explanation that is shown to the user.
The actual implementation would depend on the components used and on
how the output of the verbalization component can be integrated into the re-
sulting visualization.
With the proposed approach, when domain experts encounter the example
ontology in Figure 1, they would not have to guess what the elements of this
graphical notation mean. Instead, they can just ask the system to explain the
notation using the ontology that they are exploring. When the user clicks on
the edge likes in Figure 1 (in either visual notation), the system would show
8
Extending OWL Ontology Visualizations with Interactive Contextual Verbalization
OWL Ontology
Collector
of Relevant
Visualizer Verbalizer
Relevant Axioms
Axioms
User Selection
A B
Every C is a B
C
Ontology Visualization Verbalization of
User Selection
Fig. 2. Architecture of a contextual ontology verbalizer
the verbalization that unambiguously explains the complete meaning of this
graphical element:
Everything that likes something is a person. Everything that is liked by
something is a person.
By applying the proposed approach and by using natural language to interac-
tively explain what the graphical notation means, developers of graphical OWL
editors and viewers can enable users (domain experts in particular) to avoid
misinterpretations of ontology elements and their underlying axioms, resulting
in a better understanding of both the ontology and the notation.
The verbalization can help users even in relatively simple cases, such as
object property declarations where user’s intuitive understanding of the domain
and range of the property might not match what is asserted in the ontology. The
verbalization of OWL axioms makes this information explicit while not requiring
users to be ontology experts. The value of contextual ontology verbalization is
even more apparent for elements whose semantics might be somewhat tricky
even for more experienced users (e.g., some, only and cardinality constraints
on properties, or OWLGrEd generalization forks with disjoint and complete
constraints).
The verbalization of ontology axioms has been shown to be helpful in teach-
ing OWL to newcomers both in practical experience reports [16] as well as in
statistical evaluations [9] and thus it would be a valuable addition to ontology
visualizations.
3 Proof of Concept: Interactive Verbalizations in
OWLGrEd Visualizations
We illustrate the approach by extending OWLGrEd ontology visualizations
(i.e. visualizations in the OWLGrEd notation used by the OWLGrEd ontol-
ogy editor and its online ontology visualization tool) with on-demand contex-
9
Extending OWL Ontology Visualizations with Interactive Contextual Verbalization
Fig. 3. The example ontology in the OWLGrEd notation with CNL verbalizations
(explanations) of selected diagram elements.
tual verbalizations of the underlying OWL axioms using Attempto Controlled
English (ACE) [5].
The interactive ontology verbalization layer allows users to inspect a particu-
lar element of the presented ontology diagram and to receive a verbal explanation
of the ontology axioms that are related to this ontology element. By clicking a
mouse pointer on an element, a pop-up widget is displayed, containing a CNL
verbalization of the corresponding axioms in Attempto Controlled English. By
default, the OWLGrEd visualizer minimizes the number of verbalization wid-
gets shown simultaneously by hiding them after a certain timeout. For users to
simultaneously see the verbalizations for multiple graphical elements, there is an
option to “freeze” the widgets and prevent them from disappearing.
3.1 Ontology Verbalization Example
A demonstration of our approach, based on an example mini-university ontology,
is available online5 . Figure 3 shows a screenshot of the OWLGrEd visualization
of this ontology containing a number of verbalizations.
These verbalizations describe the ontology elements that represent the class
Course, the object property teaches, the individual Alice and the restriction on
the class MandatoryCourse. Verbalizations are implicitly linked to the corre-
sponding elements using the element labels. While it might be less convenient
to identify the implicit links in a static image, the interactive nature of the com-
bined ontology visualization and verbalization tool makes it easier for users to
keep track of relations between diagram elements and their verbalizations.
5
http://owlgred.lumii.lv/cnl-demo
10
Extending OWL Ontology Visualizations with Interactive Contextual Verbalization
Fig. 4. A fragment of the ontology visualization example showing the teaches object
property (highlighted).
To illustrate the verbalization functionality, let us look at the object property
teaches, represented in the diagram by an edge connecting the class Teacher to
the class Course (Figure 4). It leads to the following ACE verbalization:
(V1) Every teacher teaches at most 2 courses.
(V2) Everything that is taught by something is a course.
(V3) Everything that teaches something is a teacher.
(V4) If X takes Y then it is false that X teaches Y.
Note that the specific OWL terms, like disjoint, subclass and inverse, are not
used in the ACE statements. The same meaning is expressed implicitly – via
paraphrasing – using more general common sense constructions and terms.
In this case, the edge represents not only the domain and range axioms of
the property (verbalized stentences V2, V3) but also the cardinality of its range
(V1) and the restriction that teaches is disjoint with takes (expressed by the
if-then statement in V4).
Further information about combining interactive contextual CNL verbaliza-
tion with OWLGrEd ontology visualizations is available at [10].
4 Discussion
This section discusses the use of contextual ontology verbalization, focusing on
its applicability to various graphical notations, multilinguality and the potential
limitations of the approach.
11
Extending OWL Ontology Visualizations with Interactive Contextual Verbalization
4.1 Applicability to Different Visual Notations
The proposed approach is applicable to any ontology visualization where graph-
ical elements represent one or more OWL axioms. The value of using verbaliza-
tion functionality is higher for more complex notations (e.g., OWLGrEd) where
graphical elements may represent multiple axioms but even in simple graphical
notations, where each graphical element corresponds to one axiom, users will
need to know how to read the notation. Contextual ontology verbalization ad-
dresses this need by providing textual explanations of diagram elements and the
underlying OWL axioms.
A more challenging case is notations where some OWL axioms are repre-
sented as spatial relations between the elements and are not directly represented
by any graphical elements (e.g., Concept Diagrams represent subclass-of rela-
tions as shapes that are included in one another [19]). In order to represent
these axioms in ontology verbalization they need to be “attached” to one or
more graphical elements that these axioms refer to. As a result, they will be
included in verbalizations of relevant graphical elements. In the case of Con-
cept Diagrams, the subclass-of relation, which is represented by shape inclusion,
would be verbalized as part of the subclass shape.
4.2 Lexical Information and Multilinguality
In order to generate lexically and grammatically well-formed sentences, addi-
tional lexical information may need to be provided, e.g., that the property
teaches is verbalized using the past participle form “taught” in the passive voice
(inverse-of ) constructions or that the class MandatoryCourse is verbalized as a
multi-word unit “mandatory course”.
In terms of multilinguality, contextual verbalizations can be generated for
multiple languages, assuming that the translation equivalents are available for
the lexical labels of ontology symbols. This would allow domain experts to ex-
plore ontologies in their native language and avoid information getting lost in
translation.
An appropriate and convenient means for implementing a multilingual OWL
verbalization grammar is Grammatical Framework (GF) [15] which provides
a reusable resource grammar library for about 30 languages, facilitating rapid
implementation of multilingual CNLs6 . Moreover, an ACE grammar library
based on the GF resource grammar library is already available for about 10
languages [2]. This allows for using English-based entity names and the OWL
subset of ACE as an interlingua, following the two-level OWL-to-CNL approach
suggested in [6].
We have used this GF-based approach in order to provide an experimental
support for lexicalization and verbalization in OWLGrEd tools for both English
and Latvian, a highly inflected Baltic language.
6
http://www.grammaticalframework.org/
12
Extending OWL Ontology Visualizations with Interactive Contextual Verbalization
4.3 Limitations
Verbalization techniques that are a part of the proposed approach have the same
limitations as ontology verbalization in general. In particular, verbalization may
require additional lexical information to generate grammatically well-formed sen-
tences. To some degree, by employing good ontology design practices and naming
conventions as well as by annotating ontology entities with lexical labels, this
limitation can be overcome. Another issue is specific kinds of axioms that are
difficult or verbose to express in natural language without using terms of the
underlying formalism.
5 Related Work
To the best of our knowledge, there are no publications proposing combining
OWL ontology visualizations with contextual CNL verbalizations but there has
been a movement towards cooperation between both fields. In ontology visual-
izations, notations have been adding explicit labels to each graphical element
that describes what kind of axiom it represents. For example, in VOWL a line
representing a subclass-of relation is explicitly labeled with the text “Subclass
of”. This practice makes the notation more understandable to users as reported
in the VOWL user study where a user stated that “there was no need to use
the printed [notation reference] table as the VOWL visualization was very self-
explanatory” [12]. However, such labeling of graphical elements is only useful
in notations where each graphical element represents one axiom. In more com-
plex visualizations where one graphical element represents multiple axioms there
would be no place for all the labels corresponding to these axioms. For example,
in the OWLGrEd notation, class boxes can represent not just class definitions
but also subclass-of and disjoint classes assertions. In such cases, verbalizations
provide understandable explanations. Moreover, in some notations (e.g., Con-
cept Diagrams [19]) there might be no graphical elements at all for certain kinds
of axioms, as it was mentioned in Section 4.1.
In the field of textual ontology verbalizations there has been some exploration
of how to make verbalizations more convenient for users. One approach that
has been tried is grouping verbalizations by entities. It produces a kind of a
dictionary, where records are entities (class, property, individual), and every
record contains verbalizations of axioms that refer to this entity. The resulting
document is significantly larger than a plain, non-grouped verbalization because
many axioms may refer to multiple entities and thus will be repeated in each
entity. Nevertheless, the grouped presentation was preferred by users [22]. Our
approach can be considered a generalization of this approach, where a dictionary
is replaced by an ontology visualization that serves as a map of the ontology.
An ad-hoc combination of verbalization and visualization approaches could
be achieved using existing ontology tools such as Protégé by using separate vi-
sualization and verbalization plugins (e.g., ProtégéVOWL7 for visualization and
7
http://vowl.visualdataweb.org/protegevowl.html
13
Extending OWL Ontology Visualizations with Interactive Contextual Verbalization
ACEView8 for verbalization). However, this would not help in understanding
the graphical notation because the two views are independent, and thus a user
cannot know which verbalizations correspond to which graphical elements. Our
approach employs closer integration of the two ontology representations and pro-
vides contextual verbalization of axioms that directly correspond to the selected
graphical element, helping users in understanding the ontology and learning the
graphical notation used. The usefulness of combining ontology visualization and
verbalization is also demonstrated by a course for ontology engineering that uses
a CNL-based Fluent Editor and employs OWLGrEd visualizations for illustrat-
ing the meaning of CNL assertions9 .
6 Conclusions
Mathematical formalisms used in ontology engineering are hard to understand
for domain experts. Usually, graphical notations are suggested as a solution
to this problem. However, the graphical notations, while preferred by domain
experts, still have to be learned to be genuinely helpful in understanding. Until
now the only way to learn these notations was by reading the documentation.
In this article, we propose to combine ontology visualizations and CNL ver-
balizations in order to solve the learning problem. Using this approach the do-
main expert can interactively select a graphical element and receive the expla-
nation of what the element means. The explanation is generated by passing the
corresponding axioms of the element through one of the existing verbalization
services. The service returns natural language sentences explaining the OWL
axioms that correspond to the selected element and thus explaining what it
means.
CNL explanations can help domain experts to rapidly and independently
learn and use the graphical notation from the beginning, without extensive train-
ing, thus making it easier for domain experts to participate in ontology engineer-
ing thus solving one of the problems that hinder the adoption of Semantic Web
technologies in the mainstream industry.
Acknowledgments. This work has been supported by the Latvian State Re-
search program NexIT project No.1 “Technologies of ontologies, semantic web
and security”, the ESF project 2013/0005/1DP/1.1.1.2.0/13/APIA/VIAA/049,
and the University of Latvia Faculty of Computing project “Innovative informa-
tion technologies”.
8
http://attempto.ifi.uzh.ch/aceview/
9
http://www.slideshare.net/Cognitum/introduction-to-ontology-
engineering-with-fluent-editor-2014/19
14
Extending OWL Ontology Visualizations with Interactive Contextual Verbalization
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