Generating knowledge graphs through text mining of catalysis research related literature

• Published in: Catalysis science & technology Vol. 14; no. 19; pp. 5699 - 5713
• Main Authors: Behr, Alexander S., Chernenko, Diana, Koßmann, Dominik, Neyyathala, Arjun, Hanf, Schirin, Schunk, Stephan A., Kockmann, Norbert
• Format: Journal Article
• Language: English
• Published: Cambridge Royal Society of Chemistry 30.09.2024
• Subjects: Automation; Catalysis; Data management; Data mining; Datasets; Graphs; Information retrieval; Knowledge representation; Learning; Ontology; Recognition; Research data management; Workflow
• ISSN: 2044-4753; 2044-4761
• DOI: 10.1039/D4CY00369A

Structured research data management in catalysis is crucial, especially for large amounts of data, and should be guided by FAIR principles for easy access and compatibility of data. Ontologies help to organize knowledge in a structured and FAIR way. The increasing numbers of scientific publications call for automated methods to preselect and access the desired knowledge while minimizing the effort to search for relevant publications. While ontology learning can be used to create structured knowledge graphs, named entity recognition allows detection and categorization of important information in text. This work combines ontology learning and named entity recognition for automated extraction of key data from publications and organization of the implicit knowledge in a machine- and user-readable knowledge graph and data. CatalysisIE is a pre-trained model for such information extraction for catalysis research. This model is used and extended in this work based on a new data set, increasing the precision and recall of the model with regard to the data set. Validation of the presented workflow is presented on two datasets regarding catalysis research. Preformulated SPARQL-queries are provided to show the usability and applicability of the resulting knowledge graph for researchers.