Start Submission Become a Reviewer

Reading: A Semantic-Driven Knowledge Representation Model for the Materials Engineering Application

Download

A- A+
dyslexia friendly

Research Papers

A Semantic-Driven Knowledge Representation Model for the Materials Engineering Application

Authors:

Xin Cheng,

1) School of Computer and Communication Engineering, University of Science and Technology Beijing 2) Beijing Key Laboratory of Knowledge Engineering for Materials Science
X close

Changjun Hu ,

1) School of Computer and Communication Engineering, University of Science and Technology Beijing 2) Beijing Key Laboratory of Knowledge Engineering for Materials Science
X close

Yang Li

1) School of Computer and Communication Engineering, University of Science and Technology Beijing 2) Beijing Key Laboratory of Knowledge Engineering for Materials Science
X close

Abstract

A Materials Engineering Application (MEA) has been presented as a solution for the problems of materials design, solutions simulation, production and processing, and service evaluation. Large amounts of data are generated in the MEA distributed and heterogeneous environment. As the demand for intelligent engineering information applications increases, the challenge is to effectively organize these complex data and provide timely and accurate on-demand services. In this paper, based on the supporting environment of Open Cloud Services Architecture (OCSA) and Virtual DataSpace (VDS), a new semantic-driven knowledge representation model for MEA information is proposed. Faced with the MEA constantly changing user requirements, this model elaborates the semantic representation of data, services and their relationships to support the construction of domain knowledge ontology. Then, based on the ontology modeling in VDS, the semantic representations of association mapping, rule-based reasoning, and evolution tracking are analyzed to support MEA knowledge acquisition. Finally, an application example of knowledge representation in the field of materials engineering is given to illustrate the proposed model, and some experimental comparisons are discussed for evaluating and verifying the effectiveness of this method.
DOI: http://doi.org/10.2481/dsj.13-061
How to Cite: Cheng, X., Hu, C. & Li, Y., (2014). A Semantic-Driven Knowledge Representation Model for the Materials Engineering Application. Data Science Journal. 13, pp.26–44. DOI: http://doi.org/10.2481/dsj.13-061
94
Views
38
Downloads
2
Citations
Published on 24 Apr 2014.
Peer Reviewed

Downloads

  • PDF (EN)

    comments powered by Disqus