68
4 Semantic Technology for Simulations and Molecular Particle-Based Methods
At the syntactic level, we are comparing an XSD schema and OWL DL ontologies:
what are entities (say, “Software”) and attributes (say, “license” and “softwareVersion”) within EngMeta, typically correspond to classes within VIMMP ontologies
(in this case, viso:software_tool) and to the objects or data a relation points to (in
this case, a viso:license, pointed to by viso:has_license and a xs:string, pointed to by
viso:has_version_identifier).
In general, to match or integrate two assets that, like here, differ syntactically and
(even if slightly) semantically, one can think of performing the operation in two steps:
a syntactic conversion first, then a semantic matching or integration (cf. Sect. 5.3).
4.6 Closing Thoughts
Clearly, there are many concepts involved here: formalization, standardization and
automation. One could argue that for the domain we are interested in, i.e. simulations
of materials, physics and mathematics are already universal languages: why, where
and what kind of further formalization and standardization are needed?
For example, imagine we are given a set of equations that models the mixing of two
fluids in an industrial device. Even if the mathematical formulation will be accessible
to everybody with a scientific background, this does not capture the context the model
is embedded in (in fact, the simulation intent, the assumptions and approximations
made are normally expressed in natural language in an accompanying paper), and
understanding it will require delving into a jungle of details. Also, importantly, the
tacit assumptions and the technical jargon can vary a lot across communities (with
the same algorithm having a different name and so on). Classifications and standardization can therefore help inter-community communication and collaboration and
facilitate intra-community reuse of models. Coming to automation, of course, the
possibility to generate source code from a pseudocode is very appealing, both for
non-experts and for experts.
References
1. T.R. Gruber, Toward principles for the design of ontologies used for knowledge sharing? Int.
J. Human-Comput. Stud. 43(5), 907–928 (1995)
2. P. Borst, H. Akkermans, J. Top, Engineering ontologies. Int. J. Human-Comput. Stud. 46(2–3),
365–406 (1997)
3. C. Turnitsa, J.J. Padilla, A. Tolk, Ontology for modeling and simulation, in Proceedings of
WSC, ed. by B. Johansson, S. Jain, J. Montoya Torres (IEEE, Piscataway, New Jersey, USA,
2010), pp. 643–651
4. H. Cheong, A. Butscher, Physics-based simulation ontology: an ontology to support modelling
and reuse of data for physics-based simulation. J. Eng. Des. 30(10–12, SI):655–687 (2019)
5. S. Robinson, Conceptual modelling for simulation Part I: definition and requirements. J. Oper.
Res. Soc. 59(3), 278–290 (2008)
4 Semantic Technology for Simulations and Molecular Particle-Based Methods
At the syntactic level, we are comparing an XSD schema and OWL DL ontologies:
what are entities (say, “Software”) and attributes (say, “license” and “softwareVersion”) within EngMeta, typically correspond to classes within VIMMP ontologies
(in this case, viso:software_tool) and to the objects or data a relation points to (in
this case, a viso:license, pointed to by viso:has_license and a xs:string, pointed to by
viso:has_version_identifier).
In general, to match or integrate two assets that, like here, differ syntactically and
(even if slightly) semantically, one can think of performing the operation in two steps:
a syntactic conversion first, then a semantic matching or integration (cf. Sect. 5.3).
4.6 Closing Thoughts
Clearly, there are many concepts involved here: formalization, standardization and
automation. One could argue that for the domain we are interested in, i.e. simulations
of materials, physics and mathematics are already universal languages: why, where
and what kind of further formalization and standardization are needed?
For example, imagine we are given a set of equations that models the mixing of two
fluids in an industrial device. Even if the mathematical formulation will be accessible
to everybody with a scientific background, this does not capture the context the model
is embedded in (in fact, the simulation intent, the assumptions and approximations
made are normally expressed in natural language in an accompanying paper), and
understanding it will require delving into a jungle of details. Also, importantly, the
tacit assumptions and the technical jargon can vary a lot across communities (with
the same algorithm having a different name and so on). Classifications and standardization can therefore help inter-community communication and collaboration and
facilitate intra-community reuse of models. Coming to automation, of course, the
possibility to generate source code from a pseudocode is very appealing, both for
non-experts and for experts.
References
1. T.R. Gruber, Toward principles for the design of ontologies used for knowledge sharing? Int.
J. Human-Comput. Stud. 43(5), 907–928 (1995)
2. P. Borst, H. Akkermans, J. Top, Engineering ontologies. Int. J. Human-Comput. Stud. 46(2–3),
365–406 (1997)
3. C. Turnitsa, J.J. Padilla, A. Tolk, Ontology for modeling and simulation, in Proceedings of
WSC, ed. by B. Johansson, S. Jain, J. Montoya Torres (IEEE, Piscataway, New Jersey, USA,
2010), pp. 643–651
4. H. Cheong, A. Butscher, Physics-based simulation ontology: an ontology to support modelling
and reuse of data for physics-based simulation. J. Eng. Des. 30(10–12, SI):655–687 (2019)
5. S. Robinson, Conceptual modelling for simulation Part I: definition and requirements. J. Oper.
Res. Soc. 59(3), 278–290 (2008)