Non-Structured Materials Science Data Sharing Based on Semantic Annotation

• Main Authors: Changjun Hu, Chunping Ouyang, Jinbin Wu, Xiaoming Zhang, Chongchong Zhao
• Format: Article
• Language: English
• Published: Ubiquity Press 2009-04-01
• Series: Data Science Journal
• Subjects: Non-structured data; Materials science image; Data sharing; Domain knowledge ontology; Semantic annotation; Metallographic image ontology
• Online Access: http://datascience.codata.org/articles/212

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.