Chemical reaction network knowledge graphs: the OntoRXN ontology
Abstract The organization and management of large amounts of data has become a major point in almost all areas of human knowledge. In this context, semantic approaches propose a structure for the target data, defining ontologies that state the types of entities on a certain field and how these entit...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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BMC
2022-05-01
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| Series: | Journal of Cheminformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13321-022-00610-x |
| _version_ | 1811257845511880704 |
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| author | Diego Garay-Ruiz Carles Bo |
| author_facet | Diego Garay-Ruiz Carles Bo |
| author_sort | Diego Garay-Ruiz |
| collection | DOAJ |
| description | Abstract The organization and management of large amounts of data has become a major point in almost all areas of human knowledge. In this context, semantic approaches propose a structure for the target data, defining ontologies that state the types of entities on a certain field and how these entities are interrelated. In this work, we introduce OntoRXN, a novel ontology describing the reaction networks constructed from computational chemistry calculations. Under our paradigm, these networks are handled as undirected graphs, without assuming any traversal direction. From there, we propose a core class structure including reaction steps, network stages, chemical species, and the lower-level entities for the individual computational calculations. These individual calculations are founded on the OntoCompChem ontology and on the ioChem-BD database, where information is parsed and stored in CML format. OntoRXN is introduced through several examples in which knowledge graphs based on the ontology are generated for different chemical systems available on ioChem-BD. Finally, the resulting knowledge graphs are explored through SPARQL queries, illustrating the power of the semantic approach to standardize the analysis of intricate datasets and to simplify the development of complex workflows. Graphical Abstract |
| first_indexed | 2024-04-12T18:03:54Z |
| format | Article |
| id | doaj.art-027153f147b644e68856e200d3fd536d |
| institution | Directory Open Access Journal |
| issn | 1758-2946 |
| language | English |
| last_indexed | 2024-04-12T18:03:54Z |
| publishDate | 2022-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Cheminformatics |
| spelling | doaj.art-027153f147b644e68856e200d3fd536d2022-12-22T03:22:03ZengBMCJournal of Cheminformatics1758-29462022-05-0114111210.1186/s13321-022-00610-xChemical reaction network knowledge graphs: the OntoRXN ontologyDiego Garay-Ruiz0Carles Bo1Institute of Chemical Research of Catalonia (ICIQ), The Barcelona Institute of Science and TechnologyInstitute of Chemical Research of Catalonia (ICIQ), The Barcelona Institute of Science and TechnologyAbstract The organization and management of large amounts of data has become a major point in almost all areas of human knowledge. In this context, semantic approaches propose a structure for the target data, defining ontologies that state the types of entities on a certain field and how these entities are interrelated. In this work, we introduce OntoRXN, a novel ontology describing the reaction networks constructed from computational chemistry calculations. Under our paradigm, these networks are handled as undirected graphs, without assuming any traversal direction. From there, we propose a core class structure including reaction steps, network stages, chemical species, and the lower-level entities for the individual computational calculations. These individual calculations are founded on the OntoCompChem ontology and on the ioChem-BD database, where information is parsed and stored in CML format. OntoRXN is introduced through several examples in which knowledge graphs based on the ontology are generated for different chemical systems available on ioChem-BD. Finally, the resulting knowledge graphs are explored through SPARQL queries, illustrating the power of the semantic approach to standardize the analysis of intricate datasets and to simplify the development of complex workflows. Graphical Abstracthttps://doi.org/10.1186/s13321-022-00610-xOntologiesReaction networksSemanticsReactivity |
| spellingShingle | Diego Garay-Ruiz Carles Bo Chemical reaction network knowledge graphs: the OntoRXN ontology Journal of Cheminformatics Ontologies Reaction networks Semantics Reactivity |
| title | Chemical reaction network knowledge graphs: the OntoRXN ontology |
| title_full | Chemical reaction network knowledge graphs: the OntoRXN ontology |
| title_fullStr | Chemical reaction network knowledge graphs: the OntoRXN ontology |
| title_full_unstemmed | Chemical reaction network knowledge graphs: the OntoRXN ontology |
| title_short | Chemical reaction network knowledge graphs: the OntoRXN ontology |
| title_sort | chemical reaction network knowledge graphs the ontorxn ontology |
| topic | Ontologies Reaction networks Semantics Reactivity |
| url | https://doi.org/10.1186/s13321-022-00610-x |
| work_keys_str_mv | AT diegogarayruiz chemicalreactionnetworkknowledgegraphstheontorxnontology AT carlesbo chemicalreactionnetworkknowledgegraphstheontorxnontology |