Document Type

Poster

Publication Date

Summer 8-6-2024

Abstract

Knowledge management of the literature and historical data is critical to accelerated drug and materials discovery. Currently, literature knowledge is scattered in journal articles in various formats: diagrams, texts, plots, etc. Historical data from past experiments are saved in a number of local computers under confusing folder structures with ambiguous file names. To manage and organize historical data and knowledge, our group (SDLE) at CWRU follows FAIR (Findable, Accessible, Interoperable, Reusable) principles, which outline the best practices for data stewardship and data provenance, and ontology, a formal representation of terms and concepts and their relationships, as a tool to improve interoperability. Knowledge graphs, constructed from graph data structure, are built from historical knowledge and domain ontology, which acts as a schema layer, and are capable of inductive reasoning via graph traversal. In this project, an ontology for conducting a chemical reaction or synthesis is developed by mapping terms from multiple common mid-level ontologies from the chemistry domain such as Chemical Entities with Biological Interest (ChEBI), National Cancer Institute thesaurus (NCIt), Chemical Method Ontology (CHMO), etc. The ontology is built using FAIRmaterials, a package available in R and Python developed by SDLE students. The resulting ontology will be used to build a knowledge graph on the nitration of aromatic compounds with flow chemistry.

Keywords

chemistry, ontology, ChEBI, knowledge management, SDLE, FAIRmaterials

Publication Title

Data Science in Engineering and Life Sciences Symposium

Rights

© The Author(s)

Creative Commons License

Creative Commons Attribution-NonCommercial 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

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