On the Implementations of theBiTemporal RDF Model: An Experimental Approach

The BiTemporal RDF (BiTRDF) model extends the standard RDF data model by integrating both valid time and transaction time, thus enabling the representation and querying of dynamic and historical knowledge. While the theoretical foundations of BiTRDF have been established, practical implementation strategies have not yet been systematically studied. This paper bridges this gap by exploring six alternative approaches to implementing BiTRDF, combining object-oriented programming and database-oriented designs using Python and PostgreSQL. We evaluate these approaches using six synthetic datasets ranging from 0.5 million to 16 million bitemporal triples. The evaluation focuses on memory consumption, data-loading time, and query performance as data load increases. The results show that all approaches perform comparably when the knowledge store fits in memory. As the dataset size grows beyond available RAM, database-oriented implementations achieve substantially better loading and query performance, while object-oriented implementations offer greater flexibility and extensibility. These findings demonstrate the feasibility of implementing BiTRDF using existing technologies and provide practical guidance for selecting appropriate implementation strategies based on data size, performance requirements, and extensibility needs.