=Paper=
{{Paper
|id=Vol-3805/ICBO-2022_paper_4112
|storemode=property
|title=Collaborative Development of a Process Chemistry Domain Ontology, PROCO
|pdfUrl=https://ceur-ws.org/Vol-3805/ICBO-2022_paper_4112.pdf
|volume=Vol-3805
|authors=Wes Schafer,Jan Nespor,Vincent Antonucci,Yongqun Oliver He,Anna Dunn,Zachary E.X. Dance
|dblpUrl=https://dblp.org/rec/conf/icbo/SchaferNAHDD22
}}
==Collaborative Development of a Process Chemistry Domain Ontology, PROCO==
<pdf width="1500px">https://ceur-ws.org/Vol-3805/ICBO-2022_paper_4112.pdf</pdf>
<pre>
                         Collaborative Development of a Process Chemistry Domain
                         Ontology, PROCO
                         Wes Schafer1, Vincent Antonucci1, Yongqun Oliver He2 , Anna Dunn3, Zach E.X. Dance3, Jan
                         Nespor4 and Lama Saeeda4
                         1
                           Research and Development Sciences IT, Merck & Co., Inc., Rahway, NJ, USA
                         2
                           University of Michigan Medical School, Ann Arbor, MI, USA.
                        3
                           Analytical Research & Development, Merck & Co., Inc., Rahway, NJ, USA
                       4
                          IT Eng., Dev. & Integration, MSD, Prague


                                         Abstract
                                         Process chemists embracing data mining, machine learning and artificial intelligence rapidly
                                         discover that the lack of structured data hampers their efforts. Although raw and processed
                                         instrument data have been addressed with public ontologies such as Allotrope and somewhat
                                         by commercial enterprise content management solutions, the chemical context of that data has
                                         been largely neglected. Recognizing this foundational gap and the impact it would have on
                                         developing new and better scientific data capture systems like electronic laboratory
                                         notebooks, Merck chemists reached out to peers in other companies and academia to develop
                                         PROCO, a domain ontology around process chemistry that studies the development and
                                         optimization of the production processes for chemical compounds. The scope was set using
                                         specific use cases and is being rounded out modeling public databases such as ORD.

                                         Development was based on “up-scaling” the chemists semantic skill sets and the domain
                                         knowledge of the ontologists. Simplified public ontology tools such as WebProtege provided
                                         a collaborative online space for ontology developers and subject matter experts with features
                                         like commenting, suggesting and approving changes. To increase internal adoption and
                                         applicability of PROCO, the ontology was loaded into Merck’s master ontology for discovery,
                                         pre-clinical and early development space (MDO) via its CENtree ontology management
                                         system. Specific applications use MDO as the master source to develop their ‘application
                                         ontologies’. As an example, ELN application ontology covers experimental metadata and
                                         feeds it into the Perkin-Elmer Signals notebook to define values of drop-down lists in the
                                         notebook. This enables standardized data capture which consequently makes the data
                                         interoperable and reusable for analytics / data science. Using ontologies as a metadata input
                                         for data capture enables data to be ‘born FAIR’ (findable, accessible, interoperable, and
                                         reusable) which is significantly more efficient and less expensive than FAIRifying the data at
                                         later stages.

                                         This industrial and academic collaboration proved to be an effective means of achieving better
                                         structured data with limited enterprise resources. The final PROCO ontology has been
                                         submitted to the OBO Foundry to broaden the development pool and usage of the ontology.

                                         Keywords
                                         PROCO, Process Chemistry Ontology, process chemistry, ontology.

                     ICBO 2022, September 25-28, 2022, Ann Arbor, MI, USA
                     EMAIL: yongqunh@med.umich.edu (A. 1)
                     ORCID: 0000-0001-9189-9661 (A. 1)
                                  ©️ 2020 Copyright for this paper by its authors. Use permitted under Creative
                                  Commons License Attribution 4.0 International (CC BY 4.0).

                                  CEUR Workshop Proceedings (CEUR-WS.org)




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