OntoKin: An Ontology for Chemical Kinetic Reaction Mechanisms

Abstract

An ontology for capturing both data and the semantics of chemical kinetic reaction mechanisms has been developed. Such mechanisms can be applied to simulate and understand the behaviour of chemical processes, for example, the emission of pollutants from internal combustion engines. An ontology development methodology was used to produce the semantic model of the mechanisms, and a tool was developed to automate the assertion process. As part of the development methodology, the ontology is formally represented using OWL, assessed by domain experts and validated by applying a 1 reasoning tool. The resulting ontology, termed OntoKin, has been used to represent example mechanisms from the literature. OntoKin and its instantiations are integrated to create a Knowledge Base (KB), which is deployed using the RDF4J triple store. The use of the OntoKin ontology and the KB is demonstrated for three use cases-querying across mechanisms, modelling atmospheric pollution dispersion and a mechanism browser tool. As part of the query use-case, the OntoKin tools have been applied by a chemist to identify variations in the rate of a prompt NO x formation reaction in the combustion of ammonia as represented by four mechanisms in the literature.

Automated Calibration of a Poly(oxymethylene) Dimethyl Ether Oxidation Mechanism Using the Knowledge Graph Technology

“…Populating the knowledge graph is managed by a tailor-made tool set developed in this work for generating OntoChemExp-conformant OWL files. The tool set is based on that developed by Farazi et al 12 for converting chemical mechanisms to the format of OntoKin. Experimental data related to PODE 3 were manually constructed in the OWL format of OntoChemExp and then uploaded to the knowledge graph.…”

“…In the context of combustion chemistry, we have developed OntoKin 12 as an ontology for representing chemical kinetic reaction models. We have also developed OntoCompChem, 13 based on chemical markup language, 14 to store quantum chemistry calculations.…”

Combustion in the future: The importance of chemistry

“…Further opportunities arise regarding databases, frameworks, methodologies, and platforms for chemistry and reaction engineering [465] , [466] , [467] , [468] , [469] , [470] . Systematic and consistent approaches to numerical simulation of reacting systems [465] , preserving fundamental model details while saving computational time [466 , 467] , facile intercomparison of mechanisms and kinetic information [468 , 469] , recommendation of reaction routes to optimize synthesis strategies [470] , environmental information considering real-time combustion emissions and their local dispersion [469 , 472] are only a few examples that may spark off further ideas and applications for chemical processes and energy conversion systems [470] , [471] , [472] , [473] . The need for high-quality experimental data should, however, be stressed again in this context to ensure that simulations and models remain physically trustworthy.…”

The impact of intelligent cyber-physical systems on the decarbonization of energy

“…Such ontologies contain explicit descriptions of notions (concepts) for different domains so that heterogeneous data from different sources can be integrated into an interconnected knowledge graph. For example, in Figure 15, OntoKin contains ontological descriptions of chemical reaction mechanisms (which could be used for calculating emissions from combustion in engines or chemical processes in the atmosphere) 164 ; OntoCAPE has detailed information about the energy conversion unit, equipment and process 165 ; the weather ontology, DBpedia and OntoCityGML provide information about weather, common sense and urban infrastructure respectively 166,167 . A key difference between a knowledge graph and classic relational database is that both data (instances) and concepts are represented by unique IRI which can be easily extended, both in terms of instances and concepts.…”