Abstract
RDF(S) is widely used in the fields of semantic extraction, unified organization and intelligent processing of big data due to its machine understandability. Considering that classical RDF(S) can only express static semantic information, many temporal RDF models have been proposed. However, the automatic construction of temporal RDF based on different data sources, especially the temporal relational database that has been extensively researched and commercialized, is still an urgent problem to be solved. Therefore, we put forward, in this paper, a general temporal RDF(S) model, which contains a temporal RDFS layer and a temporal RDF layer. On the basis, we propose a method including mapping rules and building algorithms of extracting data from temporal relational databases and building corresponding temporal RDF(S). The method not only consider the relational semantics and constraints of the database, but also consider the temporal semantics, which can fully extract semantic information contained in the database. Finally, a prototype system named tRDB2tRDF is implemented to validate method performance. The experimental results show that our method is feasible and effective.
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