=Paper=
{{Paper
|id=Vol-1660/demo-paper1
|storemode=property
|title=Menthor Editor: An Ontology-Driven Conceptual Modeling Platform
|pdfUrl=https://ceur-ws.org/Vol-1660/demo-paper1.pdf
|volume=Vol-1660
|authors=João Moreira,Tiago Prince Sales,John Guerson,Bernardo Ferreira Bastos Braga,Freddy Brasileiro,Vinicius Sobral
|dblpUrl=https://dblp.org/rec/conf/fois/MoreiraSGBBS16
}}
==Menthor Editor: An Ontology-Driven Conceptual Modeling Platform==
https://ceur-ws.org/Vol-1660/demo-paper1.pdf
Menthor Editor: an ontology-driven
conceptual modeling platform
João MOREIRAa,1, Tiago Prince SALES b,c,d, John GUERSON c,d, Bernardo Ferreira
Bastos BRAGA c,d, Freddy BRASILEIRO c,d, Vinicius SOBRAL c
a
University of Twente, Enschede, The Netherlands
b
Institute of Cognitive Sciences and Technologies ISTC CNR
Laboratory for Applied Ontology (LOA), Trento, Italy
c
Federal University of Espírito Santo,
Ontology & Conceptual Modeling Research Group (NEMO), Vitória (ES), Brazil
d
Menthor company© , Vitória (ES), Brazil
Abstract. The lack of well-founded constructs in ontology tools can lead to the
construction of non-intended models. In this demonstration we present the
Menthor Editor, an ontology-driven conceptual modelling platform which
incorporates the theories of the Unified Foundational Ontology (UFO). We
illustrate how UFO categories can improve the design of domain ontologies.
Moreover, the verification and validation approaches are demonstrated with
ontologies of our catalogue. The complete execution of the model-driven
engineering is exemplified, including situation modelling.
Keywords. Ontology-driven Conceptual Modelling, UFO, OntoUML, Menthor.
1. Introduction
A challenge to the modelling of ontologies is the lack of well-founded structural and
temporal constructs of the conventional design techniques. Ontology-driven conceptual
modelling has been successfully applied to overcome this issue, where ontological
analysis based on a foundational ontology supports the development of well-founded
ontologies. In this demo we cover an ontologically well founded language named
OntoUML, which is based on The Unified Foundational Ontology (UFO) [1]. Also, we
present a model-driven engineering (MDE) platform that supports OntoUML
modelling. This modelling tool has been developed as an academic effort for several
years under the name of OntoUML Lightweight Editor (OLED) [2-5]. Recently, OLED
has been entirely refactored and transformed into a commercial tool, named Menthor
Editor2. Our goal is to demonstrate how theories behind UFO research can be used in
practice by exemplifying Menthor Editor’s features with diverse domain ontologies
from our catalogue. These features include the use of UFO stereotypes and inherited
rules, the model verification and validation approach and situation modelling. This
paper is structured as follows: Section 2 presents the ontology-driven conceptual
1
Corresponding Author.
2
http://www.menthor.net/
modelling process and some capabilities of Menthor Editor. Section 3 describes what
will be demonstrated and how the contribution will be illustrated interactively. Finally,
we conclude the paper with the expected contributions with this demo.
2. Menthor platform
In this section we describe the main features of Menthor Editor, following the full
description presented in [2]. First, we describe the MDE approach to ontology
engineering used in the editor. Second, we present some of the main features for model
verification and validation, such as syntactic rules with OCL support and visual
simulation with Alloy Analyzer. Then, we introduce on going work with the editor for
the Situation Modelling Language (SML) in EA, which is integrated to OntoUML and
used in the visual simulation process. Finally, we discuss how OntoUML models can
be transformed to OWL and SWRL following some design criteria. Figure 1 illustrates
the ontological MDE approach, where greyed activities are supported by the Menthor
Editor. In this approach, the cyclical process of modelling, verifying and validating is
performed until the domain ontology achieves the intended quality.
Figure 1. Ontological model-driven engineering supported by Menthor.
There are two ways to model domain ontologies with the Menthor Editor. First, the
tool provides a class diagram interface with OntoUML stereotypes (Figure 2). Second,
Sparx’s Enterprise Architecture 3 (EA) tool may be used for modelling, where the
models may be exported to Menthor Editor using an OntoUML plug-in for EA, i.e. a
UML profile that reflects OntoUML meta-model, implemented with the MDG
technology 4 . Domain ontologies are modelled in OntoUML, having constraints
formalized with OCL. Menthor Editor provides an OCL editor with syntax verification
(parsing) to textual constraints, as well as syntax highlight and code-completion.
Moreover, Menthor supports the representation of dynamic invariants through temporal
OCL, a method depict in [6].
The OntoUML meta-model in Menthor Editor is defined in ECore and
incorporates the syntactical rules of the OntoUML language. Automatic verification of
these rules is supported by the Menthor Editor, assuring that the domain ontology
respects the syntactical rules of OntoUML.
Validation can be performed to rule out unintended state of affairs through visual
simulation and anti-pattern detection. Ontological anti-patterns “are configurations that
when used in a model will typically cause the set of valid (possible) instances of that
3
http://www.sparxsystems.eu/enterprisearchitect/
4
http://www.sparxsystems.com.au/resources/mdg_tech/
model to differ from the set of instances representing intended state of affairs in that
domain” [2]. Menthor Editor has a catalogue of anti-patterns, described in [7] and an
anti-pattern management process with automatic detection, guided analysis and
automatic refactoring. Visual simulation is provided through Alloy Analyzer, which
automatically generates object diagrams of instances of the model that the user may
inspect to find if the model can represent intended or unintended state of affairs.
Figure 2. A domain ontology in Menthor interface.
Ongoing work in SML is described in [8] and includes the creation of a SML editor in
EA, following a similar approach of the OntoUML plug-in. Therefore, situation types
can be modelled in EA, having structural aspects defined with OntoUML. The designer
can validate the situation models by exporting them (via XMI) to the Menthor
Editor.SML provides the specification of the notion of a situation, i.e. a configuration
of part of reality that can be understood as a whole, and is described in [8]. Through
SML the designer can focus on the high-level patterns that emerge in time by
specifying the events that trigger a situation type. The designer defines a set of rules
among structural properties (from OntoUML) in a visual way. In Figure 3 we illustrate
a SML model of the situation type “fever”, which is triggered when the temperature of
a patient is greater than 37. The integration of SML with OntoUML was introduced in
[9] and takes advantage of the Alloy visual validation approach within Menthor Editor
[10].
Figure 3. A SML model with the situation type “fever”.
Finally, the domain ontology implementation can be automatically generated in
OWL and SWRL through model transformations, taking design decisions into account.
Menthor Editor presents a set of settings to configure the transformation approach,
including filters, axioms and data types’ selection. While requirements elicitation is not
covered by Menthor, the integration with EA enables software developers to use EA
capabilities for requirements management along with OntoUML models.
3. Demo: modelling well-founded domain ontology
We aim on demonstrating how a software developer can take advantage of the main
features of Menthor Editor’s platform, which are the result of years of research
involving UFO [1]. A number of ontologies have been developed with the Menthor
Editor (or in last versions of OLED) in diverse domains, which were assessed and
described in [7] and are available in Menthor’s model repository 5 .We plan on
exemplifying each capability of Menthor in the different domains of our catalogue. For
example, the OntoUML syntactic checker and OCL constraint editor will be illustrated
in the road traffic accident ontology, as described in [2]. In addition, the genealogical
ontology will be used to exemplify the representation of dynamic invariants with the
temporal OCL approach [6]. The validation approach with visual simulation and anti-
patterns detection will use each example of [7] to exemplify the application of semantic
anti-patterns. For example, the association cycle anti-pattern and Alloy visual
validation will be illustrated with the organizational ontology O3. The binary relation
between overlapping types anti-pattern will be illustrated with the transportation
regulation ontology (MGIC). The imprecise abstraction anti-pattern will be illustrated
with the Electrocardiogram (ECG) ontology. The relation specialization anti-pattern
will be illustrated with the OntoBio ontology. The relator mediating overlapping types
anti-pattern will be illustrated with the service ontology (UFO-S). The repeatable
relator instances anti-pattern will be illustrated with the configuration management task
ontology (CMTO). All these examples will be shown in an interactive way with the
audience, which will be able to participate and experiment with the tool.
Moreover, we plan to show the execution of the entire process in the development
of a domain ontology for the construction of a software. In particular, we plan on
illustrating how an early warning system for the detection of disease outbreaks can be
designed with Menthor Editor platform by following the example of [9]. Situation
types within this application domain, e.g. possible contagion and epidemics spread, are
specified with SML. OntoEmerge, a disaster core ontology [8], will be used as the
domain ontology representing the structural aspects and containing healthcare elements,
such as patient, hospital and exam. We will introduce common design errors in this
ontology and use Menthor Editor’s capabilities to illustrate how to address them.
Furthermore, we intend to illustrate how the specification generated in Menthor
platform with the situation type definitions can support the implementation with the
rule-based approach described in [11]. Clearly, the contribution of using Menthor
Editor to improve ontology construction by taking advantage of the inherited theories,
is better described in a demonstration session.
4. Conclusion
In this demo we intend to illustrate UFO theories in a practical way through the
Menthor Editor, the commercial tool built based on the OLED. Each feature will be
5
http://www.menthor.net/browse-models.html
illustrated with the support of domain ontologies from our catalogue. Moreover, a
complete execution of the ontological model-driven engineering approach will be
illustrated in a specific case. We expect to leverage the research in ontology-driven
conceptual modelling as a result from this demo.
References
[1] G. Guizzardi, G. Wagner, and R. D. A. Falbo, “Towards Ontological Foundations for Conceptual
Modeling: The Unified Foundational Ontology (UFO) Story,” 2015.
[2] J. Guerson, T. P. Sales, G. Guizzardi et al., “OntoUML Lightweight Editor: A Model-Based
Environment to Build, Evaluate and Implement Reference Ontologies,” 2015 IEEE 19th International
Enterprise Distributed Object Computing Workshop, pp. 144-147, 2015.
[3] G. Guizzardi, and V. Zamborlini, “Using a trope-based foundational ontology for bridging different
areas of concern in ontology-driven conceptual modeling,” Science of Computer Programming, vol. 1,
pp. 1-27, 2014.
[4] P. P. F. Barcelos, C. C. Reginato, M. E. Monteiro et al., “On the importance of truly ontological
distinctions for standardizations: A case study in the domain of telecommunications,” Computer
Standards and Interfaces, vol. 44, pp. 28-41, 2016.
[5] M. R. Blackburn, and P. O. Denno, “Using Semantic Web Technologies for Integrating Domain
Specific Modeling and Analytical Tools,” Procedia Computer Science, vol. 61, pp. 141-146, 2015.
[6] J. Guerson, and J. P. A. Almeida, “Representing Dynamic Invariants in Ontologically Well-Founded
Conceptual Models,” 2015.
[7] T. P. Sales, and G. Guizzardi, “Ontological anti-patterns: Empirically uncovered error-prone structures
in ontology-driven conceptual models,” Data and Knowledge Engineering, vol. 99, pp. 72-104, 2015.
[8] J. L. R. Moreira, L. Ferreira, M. V. Sinderen et al., “Towards ontology-driven situation-aware disaster
management,” Applied Ontology, vol. 10, pp. 339-353, 2015.
[9] V. M. Sobral, J. P. A. Almeida, and P. D. Costa, "Assessing Situation Models with a Lightweight
Formal Method," IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation
Awareness and Decision Support (CogSIMA), 2015.
[10] V. M. Sobral, "A model-driven approach to the conceptual modeling of situations: from specification to
validation," 2015.
[11] P. D. Costa, J. P. A. Almeida, I. S. A. Pereira et al., "Rule-Based Support for Situation Management,"
Fusion Methodologies in Crisis Management, 2016.