Research Papers
Non-Structured Materials Science Data Sharing Based on Semantic Annotation
Authors:
Changjun Hu ,
School of Information Engineering, University of Science and Technology Beijing, No.30 Xueyuan Road, Haidian District, Beijing 100083, China
Chunping Ouyang,
School of Information Engineering, University of Science and Technology Beijing, No.30 Xueyuan Road, Haidian District, Beijing 100083, China
Jinbin Wu,
School of Materials Science and Engineering, University of Science and Technology Beijing, No.30 Xueyuan Road, Haidian District, Beijing 100083, China
Xiaoming Zhang,
School of Information Engineering, University of Science and Technology Beijing, No.30 Xueyuan Road, Haidian District, Beijing 100083, China
Chongchong Zhao
School of Information Engineering, University of Science and Technology Beijing, No.30 Xueyuan Road, Haidian District, Beijing 100083, China
Abstract
The explosion of non-structured materials science data makes it urgent for materials researchers to resolve the problem of how to effectively share this information. Materials science image data is an important class of non-structured data. This paper proposes a semantic annotation method to resolve the problem of materials science image data sharing. This method is implemented by a four-layer architecture, which includes ontology building, semantic annotation, reasoning service, and application. We take metallographic image data as an example and build a metallographic image OWL-ontology. Users can accomplish semantic annotation of metallographic image according to the ontology. Reasoning service is provided in a data sharing application to demonstrate the effective sharing of materials science image data through adding semantic annotation.
How to Cite:
Hu, C. et al., (2009). Non-Structured Materials Science Data Sharing Based on Semantic Annotation. Data Science Journal. 8, pp.52–61. DOI: http://doi.org/10.2481/dsj.007-042
Published on 24 Apr 2009.
Peer Reviewed
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