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Knowledge Graph Approach to Combustion Chemistry and Interoperability

Authors: Arkadiusz Chadzynski; Feroz Farazi; Andreas Eibeck; Sebastian Mosbach; Markus Kraft; Markus Kraft; Leonardus Kevin Aditya; +6 Authors

Knowledge Graph Approach to Combustion Chemistry and Interoperability

Abstract

In 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.

Countries
Singapore, United Kingdom
Keywords

:Chemical engineering [Engineering], Redox Reactions, Kinetic Modeling, 004, 4017 Mechanical Engineering, Chemistry, Networking and Information Technology R&D (NITRD), Engineering::Chemical engineering, Networking and Information Technology R&D (NITRD), 4002 Automotive Engineering, QD1-999, 40 Engineering

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    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    29
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
29
Top 10%
Top 10%
Top 10%
Green
gold