Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/148878
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dc.contributor.authorFarazi, Ferozen_US
dc.contributor.authorSalamanca, Maurinen_US
dc.contributor.authorMosbach, Sebastianen_US
dc.contributor.authorAkroyd, Jethroen_US
dc.contributor.authorEibeck, Andreasen_US
dc.contributor.authorAditya, Leonardus Kevinen_US
dc.contributor.authorChadzynski, Arkadiuszen_US
dc.contributor.authorPan, Kangen_US
dc.contributor.authorZhou, Xiaochien_US
dc.contributor.authorZhang, Shaocongen_US
dc.contributor.authorLim, Mei Qien_US
dc.contributor.authorKraft, Markusen_US
dc.date.accessioned2021-06-24T09:30:42Z-
dc.date.available2021-06-24T09:30:42Z-
dc.date.issued2020-
dc.identifier.citationFarazi, F., Salamanca, M., Mosbach, S., Akroyd, J., Eibeck, A., Aditya, L. K., Chadzynski, A., Pan, K., Zhou, X., Zhang, S., Lim, M. Q. & Kraft, M. (2020). Knowledge graph approach to combustion chemistry and interoperability. ACS Omega, 5(29), 18342-18348. https://dx.doi.org/10.1021/acsomega.0c02055en_US
dc.identifier.issn2470-1343en_US
dc.identifier.other0000-0002-6786-7309-
dc.identifier.other0000-0001-6584-9097-
dc.identifier.other0000-0001-7018-9433-
dc.identifier.other0000-0002-2143-8656-
dc.identifier.other0000-0002-4293-8924-
dc.identifier.urihttps://hdl.handle.net/10356/148878-
dc.description.abstractIn this paper, we demonstrate through examples how the concept of a Semantic Web based knowledge graph can be used to integrate combustion modeling into cross-disciplinary applications and in particular how inconsistency issues in chemical mechanisms can be addressed. We discuss the advantages of linked data that form the essence of a knowledge graph and how we implement this in a number of interconnected ontologies, specifically in the context of combustion chemistry. Central to this is OntoKin, an ontology we have developed for capturing both the content and the semantics of chemical kinetic reaction mechanisms. OntoKin is used to represent the example mechanisms from the literature in a knowledge graph, which itself is part of the existing, more general knowledge graph and ecosystem of autonomous software agents that are acting on it. We describe a web interface, which allows users to interact with the system, upload and compare the existing mechanisms, and query species and reactions across the knowledge graph. The utility of the knowledge-graph approach is demonstrated for two use-cases: querying across multiple mechanisms from the literature and modeling the atmospheric dispersion of pollutants emitted by ships. As part of the query use-case, our ontological tools are applied to identify variations in the rate of a hydrogen abstraction reaction from methane as represented by 10 different mechanisms.en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.relation.ispartofACS Omegaen_US
dc.rights© 2020 American Chemical Society. This is an open access article published under a Creative Commons Attribution (CC-BY) License, which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited.en_US
dc.subjectEngineering::Chemical engineeringen_US
dc.titleKnowledge graph approach to combustion chemistry and interoperabilityen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Chemical and Biomedical Engineeringen_US
dc.contributor.organizationCambridge Centre for Advanced Research and Education in Singapore (CARES)en_US
dc.identifier.doi10.1021/acsomega.0c02055-
dc.description.versionPublished versionen_US
dc.identifier.pmid32743209-
dc.identifier.scopus2-s2.0-85088864427-
dc.identifier.issue29en_US
dc.identifier.volume5en_US
dc.identifier.spage18342en_US
dc.identifier.epage18348en_US
dc.subject.keywordsRedox Reactionsen_US
dc.subject.keywordsKinetic Modelingen_US
dc.description.acknowledgementThis work was partly funded by the National Research Foundation (NRF), Prime Minister’s Office, Singapore, under its Campus for Research Excellence and Technological Enterprise (CREATE) program, and by the European Union Horizon 2020 Research and Innovation Program under grant agreement 646121. Markus Kraft gratefully acknowledges the support of the Alexander von Humboldt foundation. The authors are grateful to EPSRC (grant number: EP/R029369/ 1) and ARCHER for financial and computational support as a part of their funding to the UK Consortium on Turbulent Reacting Flows (www.ukctrf.com) (https://doi.org/10.17863/ CAM.54397).en_US
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