Ontologies for the materials science domain
Ontologies standardize terminology in a domain and are a basis for semantically enriching data, integration of data from different databases, and reasoning over the data. They deal with the big data issues of Variety, Variability, and Veracity. They are also an important technique in ensuring FAIR (Findable, Accessible, Interoperable, and Reusable) data. The SeRC-DCMD team developed methods for developing and extending ontologies. The methods use automated text mining to generate candidate concepts and relations for the ontology that are then evaluated by a domain expert. Further, using these methods two existing ontologies in the nanotechnology domain (NanoParticle Ontology and eNanoMapper) were extended with new concepts and relations. It was shown that the quality of the ontologies was improved, which in turn improves the quality of the semantically-enabled systems that these ontologies.
- Li H, Armiento R, Lambrix P, A Method for Extending Ontologies with Application to the Materials Science Domain, Data Science Journal 18(1) (2019). http://doi.org/10.5334/dsj-2019-050
- Lambrix P, Armiento R, Delin A, Li H, Big Semantic Data Processing in the Materials Design Domain, in Sakr and Zomaya (eds), Encyclopedia of Big Data Technologies, Springer, 2018. ISBN: 978-3-319-63962-8. https://doi.org/10.1007/978-3-319-63962- 8_293-1