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4 Semantic Technology for Simulations and Molecular Particle-Based Methods
In the area of physics and engineering, we would like to point out two ontologies:
the pioneering PhysSys [2], which already gave a central role to theories such as
mereology and topology and recognized the need for different viewpoints on a given
problem, and the very recent Physics-based Simulation Ontology (PSO) [4], which
uses the Basic Formal Ontology (BFO) [8] as an upper ontology and is split into two
parts addressing the physical phenomena (PSO-Phys) and the simulation aspects
(PSO-Sim). Both ontologies focus on what in the EMMC vocabulary are called
continuum models.
Looking at solutions to characterize software in other domains, we find that, in
logistics and manufacturing, discrete-event simulations are the object of the DeMO
Ontology [9]; recent work capturing the point of view of a scientist end user has
led to the Software Ontology (SWO) [10] for life sciences and to OntoSoft [11] for
geosciences.
Moving to variables, we would like to recall the catalogue for Quantities, Units,
Dimensions and Data Types Ontologies (QUDT) [12], which addresses among others dimensional analysis and a classification of units, and the Scientific Variables
Ontology (SVO) [13, 14]. The latter originated analysing thousands of variables
in the area of natural sciences, but provides a framework that can be, in principle,
adapted to other fields.
With a focus on the software engineering aspects, we highlight instead the Software Engineering Ontology Network (SEON) [15], an ontology network based on the
Unified Foundational Ontology (UFO) [16, 17]. Connected to SEON, a Reference
Ontology on Object-Oriented Code (OOC-O) was recently proposed [18].
We note that the relation of some of these ontologies to our work is very concrete:
in fact, concepts from SWO and QUDT are currently imported in VISO, VOV and
other VIMMP ontologies (cf. Sects. 4.3 and 4.4).
4.1.1 Examples of Applications
As already explained in Chap. 1, ontologies are an explicit and formal way to represent knowledge in a certain domain. But how are they actually used in the context
of simulations and modelling?
This question connects to the purpose the ontology is designed for and to technical aspects, such as the availability and choice of tools (for example, to connect
ontologies to programming languages
1 ). And it also poses the question whether we
expect the end users to be (mainly) humans or machines.
Also, the use could be more or less direct: thinking, for example, of a database,
a triplestore would make an immediate use of the ontology, whereas a less direct
approach would be to take into account aspects of the ontology when designing the
database.
1 For example, Owlready 2 [19] is a Python module that allows to import and manipulate OWL 2.0
ontologies and do ontology-oriented programming in Python.
4 Semantic Technology for Simulations and Molecular Particle-Based Methods
In the area of physics and engineering, we would like to point out two ontologies:
the pioneering PhysSys [2], which already gave a central role to theories such as
mereology and topology and recognized the need for different viewpoints on a given
problem, and the very recent Physics-based Simulation Ontology (PSO) [4], which
uses the Basic Formal Ontology (BFO) [8] as an upper ontology and is split into two
parts addressing the physical phenomena (PSO-Phys) and the simulation aspects
(PSO-Sim). Both ontologies focus on what in the EMMC vocabulary are called
continuum models.
Looking at solutions to characterize software in other domains, we find that, in
logistics and manufacturing, discrete-event simulations are the object of the DeMO
Ontology [9]; recent work capturing the point of view of a scientist end user has
led to the Software Ontology (SWO) [10] for life sciences and to OntoSoft [11] for
geosciences.
Moving to variables, we would like to recall the catalogue for Quantities, Units,
Dimensions and Data Types Ontologies (QUDT) [12], which addresses among others dimensional analysis and a classification of units, and the Scientific Variables
Ontology (SVO) [13, 14]. The latter originated analysing thousands of variables
in the area of natural sciences, but provides a framework that can be, in principle,
adapted to other fields.
With a focus on the software engineering aspects, we highlight instead the Software Engineering Ontology Network (SEON) [15], an ontology network based on the
Unified Foundational Ontology (UFO) [16, 17]. Connected to SEON, a Reference
Ontology on Object-Oriented Code (OOC-O) was recently proposed [18].
We note that the relation of some of these ontologies to our work is very concrete:
in fact, concepts from SWO and QUDT are currently imported in VISO, VOV and
other VIMMP ontologies (cf. Sects. 4.3 and 4.4).
4.1.1 Examples of Applications
As already explained in Chap. 1, ontologies are an explicit and formal way to represent knowledge in a certain domain. But how are they actually used in the context
of simulations and modelling?
This question connects to the purpose the ontology is designed for and to technical aspects, such as the availability and choice of tools (for example, to connect
ontologies to programming languages
1 ). And it also poses the question whether we
expect the end users to be (mainly) humans or machines.
Also, the use could be more or less direct: thinking, for example, of a database,
a triplestore would make an immediate use of the ontology, whereas a less direct
approach would be to take into account aspects of the ontology when designing the
database.
1 For example, Owlready 2 [19] is a Python module that allows to import and manipulate OWL 2.0
ontologies and do ontology-oriented programming in Python.