Name
Abstract | ||
|---|---|---|
Purpose - With the rapid development of materials informatics and the Semantic Web, the semantic-driven solution has emerged to improve traditional query technology, which is hard to discover implicit knowledge from materials data. However, it is a nontrivial thing for materials scientists to construct a semantic query, and the query results are usually presented in RDF/XML format which is not convenient for users to understand. This paper aims to propose an approach to construct semantic query and visualize the query results for metallic materials domain.Design/methodology/approach - The authors design a query builder to generate SPARQL query statements automatically based on domain ontology and query conditions inputted by users. Moreover, a semantic visualization model is defined based on the materials science tetrahedron to support the visualization of query results in an intuitive, dynamic and interactive way.Findings - Based on the Semantic Web technology, the authors design an automatic semantic query builder to help domain experts write the normative semantic query statements quickly and simply, as well as a prototype (named MatViz) is developed to visually show query results, which could help experts discover implicit knowledge from materials data. Moreover, the experiments demonstrate that the proposed system in this paper can rapidly and effectively return visualized query results over the metallic materials data set.Originality/value - This paper mainly discusses an approach to support semantic query and visualization of metallic materials data. The implementation of MatViz will be a meaningful work for the research of metal materials data integration. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1108/IJWIS-11-2016-0065 | INTERNATIONAL JOURNAL OF WEB INFORMATION SYSTEMS |
Keywords | Field | DocType |
Ontology, Data visualization, Metallic materials data, Semantic query | Query optimization,Data mining,Query language,RDF query language,Query expansion,Information retrieval,Computer science,Sargable,Web query classification,SPARQL,Semantic query | Journal |
Volume | Issue | ISSN |
13 | 3 | 1744-0084 |
Citations | PageRank | References |
0 | 0.34 | 23 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoming Zhang | 1 | 263 | 35.42 |
| Huilin Chen | 2 | 0 | 0.34 |
| Yanqin Ruan | 3 | 0 | 0.34 |
| Dongyu Pan | 4 | 0 | 0.34 |
| Chongchong Zhao | 5 | 50 | 11.17 |