In the materials science domain the data-driven science paradigm has become the focus since the beginning of the 2000s. A large number of research groups and communities are building and developing data-driven workflows. However, much of the data and knowledge is stored in different heterogeneous data sources maintained by different groups.
This leads to a reduced availability of the data and poor interoperability between systems in this domain. Ontology-based techniques are an important way to reduce these problems and a number of efforts have started.
In their paper, Huanyu Li, Rickard Armiento and Patrick Lambrix, investigate efforts in the materials science, and in particular in the nanotechnology domain, and show how such ontologies developed by domain experts can be improved.
They use a phrase-based topic model approach and formal topical concept analysis on unstructured text in this domain to suggest additional concepts and axioms for the ontology that should be validated by a domain expert. Furthermore they describe the techniques and show the usefulness of the approach through an experiment where they extend two nanotechnology ontologies using approximately 600 titles and abstracts.
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How to Cite: Li, H., Armiento, R. and Lambrix, P., 2019. A Method for Extending Ontologies with Application to the Materials Science Domain. Data Science Journal, 18(1), p.50. DOI