Chapter 4
Semantic Technology for Simulations and
Molecular Particle-Based Methods
4.1 Brief Overview of Ontologies for Modelling and
Simulation
Since the appearance of ontologies in computer science in the 90s [1], there have been
proposals and endeavours to use them to describe modelling and simulation, with the
aim to support the exchange of information both between people (communication)
and between software (interoperability) [2–4].
As with any topic, clearly also in this case many different points of view can be
adopted. Browsing the literature on the subject, we identify two major perspectives:
taking a more philosophical approach, some authors focus on the process of modelling itself, as a cognitive process, and its relation to the physical world, see, e.g. [3];
on the other side, with a more application-oriented view, other authors focus on the
structuring of models and simulations, giving for granted their connection to reality
[2, 4].
The first perspective might seem surprising, but, in fact, there are various intellectual steps that are undertaken each time we use a numerical simulation to make
predictions about a certain real problem: typically these involve abstraction and simplification, to arrive to a model (in the MODA sense) and then its conversion into
a numerical implementation (see, for example, [5–7], where the steps of the first
part, what Robinson calls conceptual modelling for simulation, are described). It is
therefore relevant to be able to formally describe these steps explicitly, for example,
to compare models involving different levels of abstraction, and to address model
verification and validation.
Coming to the ontologies presented or referred to in this book, EMMO and VIVO
are close in spirit to the first perspective, whereas the ontologies we will describe in
this chapter, the VImmp Ontology of Software (VISO) and the Vimmp Ontology of
Variables (VOV), are closer in spirit to the second one. Before describing them (in
Sects. 4.3 and 4.4), we highlight in the following some existing ontologies and assets
that have a similar purpose or scope.
© The Author(s) 2021
M. Horsch et al., Data Technology in Materials Modelling,
SpringerBriefs in Applied Sciences and Technology,
https://doi.org/10.1007/978-3-030-68597-3_4
55
Semantic Technology for Simulations and
Molecular Particle-Based Methods
4.1 Brief Overview of Ontologies for Modelling and
Simulation
Since the appearance of ontologies in computer science in the 90s [1], there have been
proposals and endeavours to use them to describe modelling and simulation, with the
aim to support the exchange of information both between people (communication)
and between software (interoperability) [2–4].
As with any topic, clearly also in this case many different points of view can be
adopted. Browsing the literature on the subject, we identify two major perspectives:
taking a more philosophical approach, some authors focus on the process of modelling itself, as a cognitive process, and its relation to the physical world, see, e.g. [3];
on the other side, with a more application-oriented view, other authors focus on the
structuring of models and simulations, giving for granted their connection to reality
[2, 4].
The first perspective might seem surprising, but, in fact, there are various intellectual steps that are undertaken each time we use a numerical simulation to make
predictions about a certain real problem: typically these involve abstraction and simplification, to arrive to a model (in the MODA sense) and then its conversion into
a numerical implementation (see, for example, [5–7], where the steps of the first
part, what Robinson calls conceptual modelling for simulation, are described). It is
therefore relevant to be able to formally describe these steps explicitly, for example,
to compare models involving different levels of abstraction, and to address model
verification and validation.
Coming to the ontologies presented or referred to in this book, EMMO and VIVO
are close in spirit to the first perspective, whereas the ontologies we will describe in
this chapter, the VImmp Ontology of Software (VISO) and the Vimmp Ontology of
Variables (VOV), are closer in spirit to the second one. Before describing them (in
Sects. 4.3 and 4.4), we highlight in the following some existing ontologies and assets
that have a similar purpose or scope.
© The Author(s) 2021
M. Horsch et al., Data Technology in Materials Modelling,
SpringerBriefs in Applied Sciences and Technology,
https://doi.org/10.1007/978-3-030-68597-3_4
55