4.1 Brief Overview of Ontologies for Modelling and Simulation
57
We can get some insight on the possibilities by looking at the examples given
above: OntoSoft [11] was used to design a platform to find and compare software
[20]; the Simulation Intent Ontology [21] is used in connection with the CoGui tool
[22] to automatize some steps in the simulation setup; finally, one of the perspective
uses of SVO and the connected tools [13, 14] is to generate suitably formed variables
starting from free-form text.
From the point of view of the source code, a perspective use of OOC-O is to
support polyglot programming [18], i.e. the simultaneous use of multiple objectoriented programming languages.
4.2 Other Relevant Assets and Approaches
In the previous section, we limited the scope to ontologies; however, as discussed in
Chap. 1, the semantic spectrum is wide, and along with them there are other relevant
assets, which are technically different but similar in spirit, such as data schemas
(cf. Chap. 2).
Also, we should recall different branches of a field that is sometimes referred
to as conceptual modelling. Historically, in the ’60s–’70s, novel ideas setting the
basis of this field appeared in different areas of computer science, namely, artificial
intelligence, programming languages, databases, software engineering [23]: these
ideas lead, among others, to the development of knowledge-representation languages,
object-oriented programming and entity-relationship (ER) models (see [23] for a
discussion of the pioneering ideas in each area and a brief history of the topic).
Even if the connections between these approaches are not always direct, the thinking behind their development is similar: so, for example, when building an ontology
for a domain, it is definitely instructive to look also into object-oriented programs
and schemas for such domain, and vice versa.
As an illustration of schemas for our area, we recall the Chemical Markup Language (CML) [24] and the ThermoML schema [25, 26], a IUPAC standard primarily
developed at NIST. In the direction of object-oriented programs, a popular tool is
the Atomic Simulation Environment (ASE) [27] which allows to set up, control,
visualize and analyse simulations at the atomic and electronic level.
Another relevant topic is that of visual programming: a visual scheme is used
to represent a model (in the general sense), but also to generate the source code
(model-driven simulation). This operation can sometimes work also in the opposite
direction, extracting the model from the source code, a form of reverse engineering.
Finally, in the area of software design and business modelling, it is important to
recall the role of the Object Management Group (OMG) [28] that was formed 30
years ago; in particular, its activities lead to the development of the Unified Modeling
Language (UML) [29] and an ecosystem of specifications based on it.
57
We can get some insight on the possibilities by looking at the examples given
above: OntoSoft [11] was used to design a platform to find and compare software
[20]; the Simulation Intent Ontology [21] is used in connection with the CoGui tool
[22] to automatize some steps in the simulation setup; finally, one of the perspective
uses of SVO and the connected tools [13, 14] is to generate suitably formed variables
starting from free-form text.
From the point of view of the source code, a perspective use of OOC-O is to
support polyglot programming [18], i.e. the simultaneous use of multiple objectoriented programming languages.
4.2 Other Relevant Assets and Approaches
In the previous section, we limited the scope to ontologies; however, as discussed in
Chap. 1, the semantic spectrum is wide, and along with them there are other relevant
assets, which are technically different but similar in spirit, such as data schemas
(cf. Chap. 2).
Also, we should recall different branches of a field that is sometimes referred
to as conceptual modelling. Historically, in the ’60s–’70s, novel ideas setting the
basis of this field appeared in different areas of computer science, namely, artificial
intelligence, programming languages, databases, software engineering [23]: these
ideas lead, among others, to the development of knowledge-representation languages,
object-oriented programming and entity-relationship (ER) models (see [23] for a
discussion of the pioneering ideas in each area and a brief history of the topic).
Even if the connections between these approaches are not always direct, the thinking behind their development is similar: so, for example, when building an ontology
for a domain, it is definitely instructive to look also into object-oriented programs
and schemas for such domain, and vice versa.
As an illustration of schemas for our area, we recall the Chemical Markup Language (CML) [24] and the ThermoML schema [25, 26], a IUPAC standard primarily
developed at NIST. In the direction of object-oriented programs, a popular tool is
the Atomic Simulation Environment (ASE) [27] which allows to set up, control,
visualize and analyse simulations at the atomic and electronic level.
Another relevant topic is that of visual programming: a visual scheme is used
to represent a model (in the general sense), but also to generate the source code
(model-driven simulation). This operation can sometimes work also in the opposite
direction, extracting the model from the source code, a form of reverse engineering.
Finally, in the area of software design and business modelling, it is important to
recall the role of the Object Management Group (OMG) [28] that was formed 30
years ago; in particular, its activities lead to the development of the Unified Modeling
Language (UML) [29] and an ecosystem of specifications based on it.