Abstract
Pragmatic interoperability between platforms and service-oriented architectures exists whenever there is an agreement on the roles of participants and components as well as minimum standards for good practice. In this work, it is argued that open platforms require pragmatic interoperability, complementing syntactic interoperability (e.g., through common file formats), and semantic interoperability by ontologies that provide agreed definitions for entities and relations. For consistent data management and the provision of services in computational molecular engineering, community-governed agreements on pragmatics need to be established and formalized. For this purpose, if ontology-based semantic interoperability is already present, the same ontologies can be used. This is illustrated here by the role of the “translator” and procedural definitions for the process of “translation” in materials modelling, which refers to mapping industrial research and development problems onto solutions by modelling and simulation. For associated roles and processes, substantial previous standardization efforts have been carried out by the European Materials Modelling Council (EMMC ASBL). In the present work, the Materials Modelling Translation Ontology (MMTO) is introduced, and it is discussed how the MMTO can contribute to formalizing the pragmatic interoperability standards developed by EMMC ASBL.
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Notes
- 1.
URL: http://www.molmod.info/semantics/mmto.ttl, as of 11th May 2021.
- 2.
URL: http://www.molmod.info/semantics/osmo.ttl, as of 11th May 2021.
References
Allweyer, T.: BPMN 2.0: Introduction to the Standard for Business Process Modeling. BOD, Norderstedt, Germany, 2nd edn. (2016). ISBN 978-3-8370-9331-5
Arp, R., Smith, B., Spear, A.D.: Building Ontologies with Basic Formal Ontology. MIT Press, Cambridge, Massachusetts, USA (2015). ISBN 978-0-262-52781-1
Asher, N., Vieu, L.: Toward a geometry of common sense: A semantics and a complete axiomatization of mereotopology. In: Mellish, C. (ed.) Proceedings of the 14th IJCAI, pp. 846–852. Morgan Kaufmann, San Mateo, California, USA (1995). ISBN 978-1-55860-363-9
Barrientos, L.G., Sosa, E.R.C., García Castro, P.E.: Considerations of e-commerce within a globalizing context. Int. J. Manag. Inf. Syst. 16(1), 101–110 (2012). https://doi.org/10.19030/ijmis.v16i1.6726
Bennett, M.: Reuse of semantics in business applications. In: Guizzardi, G., et al. (eds.) Ontologies in Conceptual Modeling and Information Systems Engineering (ONTO.COM/ODISE 2014). CEUR-WS, Aachen, Germany (2014)
Borgo, S., Masolo, C.: Ontological foundations of DOLCE. In: Poli, R., Healy, M., Kameas, A. (eds.) Theory and Applications of Ontology: Computer Applications, pp. 279–295. Springer, Dordrecht, Netherlands (2010). https://doi.org/10.1007/978-90-481-8847-5_13. ISBN 978-90-481-8846-8
CEN-CENELEC Management Centre: Materials modelling: Terminology, classification and metadata. CEN workshop agreement 17284, Brussels, Belgium (2018)
Ceusters, W.: An information artifact ontology perspective on data collections and associated representational artifacts. Stud. Health Technol. Inform. 180, 68–72 (2012). https://doi.org/10.3233/978-1-61499-101-4-68
De Baas, A.F. (ed.): What makes a material function? Let me compute the ways. EU Publications Office, Luxembourg (2017). This document is by convention known as the Review of Materials Modelling (RoMM) (2017). ISBN 978-92-79-63185-6
Dykeman, D., Hashibon, A., Klein, P., Belouettar, S.: Guideline business decision support systems (BDSS) for materials modelling (2020). https://doi.org/10.5281/zenodo.4054009
Edwards, P.N., Mayernik, M.S., Batcheller, A.L., Bowker, G.C., Borgman, C.L.: Science friction: Data, metadata, and collaboration. Soc. Stud. Sci. 41(5), 667–690 (2011). https://doi.org/10.1177/0306312711413314
EMMC ASBL: European Materials and Modelling Ontology, version 1.0.0 beta (2021). https://github.com/emmo-repo/ and https://emmc.info/emmo-info/. Accessed 19th April 2021
EMMC Coordination and Support Action: EMMC Translation Case Template (2017). https://emmc.info/emmc-translation-case-template/. Accessed 11th May 2021
EMMC Coordination and Support Action: Report on business related quality attributes for industry integration of materials modelling. Project deliverable report 6.3 (2018)
Faheem, H.M., König-Ries, B., Aslam, M.A., Aljohani, N.R., Katib, I.: Ontology design for solving computationally-intensive problems on heterogeneous architectures. Sustainability 10, 441 (2018). https://doi.org/10.3390/su10020441
Farazi, F., et al.: OntoKin: an ontology for chemical kinetic reaction mechanisms. J. Chem. Inf. Model. 60(1), 108–120 (2020). https://doi.org/10.1021/acs.jcim.9b00960
Morgado, J.F., et al.: Mechanical testing ontology for digital-twins: A roadmap based on EMMO. In: Castro, R.G., Davies, J., Antoniou, G., Fortuna, C. (eds.) SeDiT 2020: Semantic Digital Twins 2020, p. 3. CEUR-WS, Aachen (2020)
Goldbeck, G., Ghedini, E., Hashibon, A., Schmitz, G.J., Friis, J.: A reference language and ontology for materials modelling and interoperability. In: Proceedings of the NAFEMS World Congress 2019, p. NWC\(\_\)19\(\_\)86. NAFEMS, Knutsford, UK (2019)
Guizzardi, G., Wagner, G.: Using the Unified Foundational Ontology (UFO) as a foundation for general conceptual modeling languages. In: Poli, R., Healy, M., Kameas, A. (eds.) Theory and Applications of Ontology: Computer Applications, pp. 175–196. Springer, Dordrecht, Netherlands (2010). https://doi.org/10.1007/978-90-481-8847-5_8. ISBN 978-90-481-8846-8
Horridge, M.: OWLViz (2010). https://protegewiki.stanford.edu/wiki/OWLViz. Accessed 11th May 2021
Horsch, M.T., et al.: Semantic interoperability and characterization of data provenance in computational molecular engineering. Fluid Phase Equilib. 65(3), 1313–1329 (2020). https://doi.org/10.1021/acs.jced.9b00739
Horsch, M.T., Chiacchiera, S., Cavalcanti, W.L., Schembera, B.: Data Technology in Materials Modelling. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68597-3.
Horsch, M.T., Chiacchiera, S., Schembera, B., Seaton, M.A., Todorov, I.T.: Semantic interoperability based on the European Materials and Modelling Ontology and its ontological paradigm: Mereosemiotics. In: Chinesta, F., Abgrall, R., Allix, O., Kaliske, M. (eds.) Proceedings of 14th WCCM-ECCOMAS 2020. Scipedia, Barcelona (2021). https://doi.org/10.23967/wccm-eccomas.2020.297
Horsch, M.T., et al.: Ontologies for the Virtual Materials Marketplace. KI - Künstliche Intell. 34(3), 423–428 (2020). https://doi.org/10.1007/s13218-020-00648-9
Hristova-Bogaerds, D., et al.: EMMC Translators’ Guide. Technical report, EMMC-CSA (2019). https://doi.org/10.5281/zenodo.3552260
Klein, P., et al.: Translation in materials modelling: Process and progress. Technical report, Zenodo (2021). https://doi.org/10.5281/zenodo.4729917
Machač, J., Steiner, F., Tupa, J.: Product life cycle risk management. In: Oudoza, C.F. (ed.) Risk Management Treatise for Engineering Professionals, pp. 51–72. IntechOpen, London, UK (2018). ISBN 978-1-78984-600-3
Mons, B.: Data Stewardship for Open Science. CRC, Boca Raton, Florida, USA (2018). ISBN 978-1-4987-5317-3
Neff, G., Tanweer, A., Fiore-Gartland, B., Osburn, L.: Critique and contribute: a practice-based framework for improving critical data studies and data science. Big Data 5(2), 85–97 (2017). https://doi.org/10.1089/big.2016.0050
Olsina, L.: Analyzing the usefulness of ThingFO as a foundational ontology for sciences. In: Pons, C., Frías, M., Anacleto, A. (eds.) XXI Simposio Argentino de Ingeniería de Software, pp. 172–191. SADIO, Buenos Aires, Argentina (2020)
Peirce, C.S.: Peirce on Signs: Writings on Semiotic. University of North Carolina Press, Chapel Hill, North Carolina, USA (1991). ISBN 978-0-80784342-0
Pezzotta, M., et al.: Report on translation case studies describing the gained experience. Technical report, EMMC ASBL (2021). https://doi.org/10.5281/zenodo.4457849
Pries, K.H., Quigley, J.M.: Reducing Process Costs with Lean, Six Sigma, and Value Engineering Techniques. CRC, Boca Raton, Florida, USA (2013). ISBN 978-1-4398-8725-7
Rospocher, M., Ghidini, C., Serafini, L.: An ontology for the business process modelling notation. In: Garbacz, P., Kutz, O. (eds.) Formal Ontology in Information Systems: Proceedings of the Eighth International Conference, pp. 133–146. IOS, Amsterdam, Netherlands (2014). ISBN 978-1-61499-437-4
Schembera, B.: Like a rainbow in the dark: metadata annotation for HPC applications in the age of dark data. J. Supercomput. (3), 1–21 (2021). https://doi.org/10.1007/s11227-020-03602-6
Schembera, B., Durán, J.M.: Dark data as the new challenge for big data science and the introduction of the scientific data officer. Philos. Technol. 33(1), 93–115 (2019). https://doi.org/10.1007/s13347-019-00346-x
Schembera, B., Iglezakis, D.: EngMeta: metadata for computational engineering. Int. J. Metadata Semant. Ontol. 14(1), 26 (2020). https://doi.org/10.1504/IJMSO.2020.107792
Schoop, M., de Moor, A., Dietz, J.: The pragmatic web: a manifesto. Comm. ACM 49(5), 75–76 (2006). https://doi.org/10.1145/1125979
Todor, A., Paschke, A., Heineke, S.: ChemCloud: chemical e-science information cloud. Nat. Prec. (2011). https://doi.org/10.1038/npre.2011.5642.1
Weber, J.H., Kuziemsky, C.: Pragmatic interoperability for eHealth systems: The fallback workflow patterns. In: 2019 IEEE/ACM 1st International Workshop on Software Engineering for Healthcare (SEH), pp. 29–36. IEEE, Piscataway, New Jersey, USA (2019). ISBN 978-1-72812252-6
Neiva, F.W., David, J.M.N., Braga, R., Campos, F.: Towards pragmatic interoperability to support collaboration: a systematic review and mapping of the literature. Informat. Softw. Technol. 72, 137–150 (2016). https://doi.org/10.1016/j.infsof.2015.12.013
Wiesner, S., Thoben, K.D.: Requirements for models, methods and tools supporting servitisation of products in manufacturing service ecosystems. Int. J. Comput. Integr. Manuf. 30(1), 191–201 (2016). https://doi.org/10.1080/0951192X.2015.1130243
Acknowledgment
The authors thank N. Adamovic, W. L. Cavalcanti, G. Goldbeck, and A. Hashibon for fruitful discussions. The co-author P.K. acknowledges funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 721027 (FORCE), the co-author N.K. under grant agreement 723867 (EMMC-CSA), the co-authors S.C., M.T.H., M.A.S., and I.T.T. under grant agreement no. 760907 (Virtual Materials Marketplace), and the co-authors N.A.K. and P.K. under grant agreement no. 952903 (VIPCOAT); the co-authors M.T.H. and B.S. acknowledge funding by the German Research Foundation (DFG) through the National Research Data Infrastructure for Catalysis-Related Sciences (NFDI4Cat), DFG project no. 441926934, within the National Research Data Infrastructure (NFDI) programme of the Joint Science Conference (GWK). This work was facilitated by activities of the Innovation Centre for Process Data Technology (Inprodat e.V.), Kaiserslautern.
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Horsch, M.T. et al. (2021). Pragmatic Interoperability and Translation of Industrial Engineering Problems into Modelling and Simulation Solutions. In: Sychev, A., Makhortov, S., Thalheim, B. (eds) Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2020. Communications in Computer and Information Science, vol 1427. Springer, Cham. https://doi.org/10.1007/978-3-030-81200-3_4
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