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.

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Abstract. Geo-spatial ontologies provide knowledge about places in the world and spatial relations between them. They are fundamental in order to build semantic information retrieval systems and to achieve semantic interoperability in geo-spatial applications. In this paper we present GeoWordNet, a semantic resource we created from the full integration of GeoNames, other high quality resources and WordNet. The methodology we followed was largely automatic, with manual checks when needed. This allowed us accomplishing at the same time a never reached before accuracy level and a very satisfactory quantitative result, both in terms of concepts and geographical entities.

In this paper, we develop a set of software agents which improve a knowledge-graph containing thermodynamic data of chemical species by means of quantum chemical calculations and error-canceling balanced reactions. The knowledge-graph represents species-associated information by making use of the principles of linked data, as employed in the Semantic Web, where concepts correspond to vertices and relationships between the concepts correspond to edges of the graph. We implement this representation by means of ontologies, which formalize the definition of concepts and their relationships, as a critical step to achieve interoperability between heterogeneous data formats and software. The agents, which conduct quantum chemical calculations and derive the estimates of standard enthalpies of formation, update the knowledge-graph with newly obtained results, improving data values, and adding nodes and connections between them. A key distinguishing feature of our approach is that it extends an existing, general-purpose knowledge-graph, called J-Park Simulator (http://theworldavatar.com), and its ecosystem of autonomous agents, thus enabling seamless cross-domain applications in wider contexts. To this end, we demonstrate how quantum calculations can directly affect the atmospheric dispersion of pollutants in an industrial emission use-case.

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