Abstract
Materials digital data, high throughput experiments and high throughput computations are regarded as three key pillars of materials genome initiatives. With the fast growth of materials data, the integration and sharing of data is very urgent, that has gradually become a hot topic of materials informatics. Due to the lack of semantic description, it is difficult to integrate data deeply in semantic level when adopting the conventional heterogeneous database integration approaches such as federal database or data warehouse. In this paper, a semantic integration method is proposed to create the semantic ontology by extracting the database schema semi-automatically. Other heterogeneous databases are integrated to the ontology by means of relational algebra and the rooted graph. Based on integrated ontology, semantic query can be done using SPARQL. During the experiments, two world famous First Principle Computational databases, OQMD and Materials Project are used as the integration targets, which show the availability and effectiveness of our method.
Highlights
The materials science heavily relies on costly experiments and simulation based methods to understand the intrinsic mechanisms of the relationships among processing-structureproperty-performance (PSPP)
The data-driven featured materials science is regarded as the essential content of materials informatics, which provides the foundations for fourth paradigm of materials discovery.[1,2]
Mapping from the relational database to ontology and other heterogeneous database integration are described in Section IV and V
Summary
The materials science heavily relies on costly experiments and simulation based methods to understand the intrinsic mechanisms of the relationships among processing-structureproperty-performance (PSPP). The big data generated by high throughput experiments and computations has provided a great opportunities for data-driven based techniques, which is one of the three pillars of materials genome initiatives (MGI). Data-driven materials techniques are playing a big role in revealing PSPP relationships in materials science, which can be used for both property prediction based forward models, and materials discovery based inverse models. In different data sources the data schema or format are quite different which makes it difficult to understand with each other. Mapping from the relational database to ontology and other heterogeneous database integration are described in Section IV and V.
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