RDFM: An alternative approach for representing, storing, and maintaining meta-knowledge in web of data

The Web of Data needs additional information, i.e., meta-knowledge to ensure quality and build the trust in data. For representing meta-knowledge, there exist various approaches in the literature, for example, RDFt, tRDF, RDF Reification, Singleton Property and Named Graph. There are various issues associated with these approaches in representing the meta-knowledge, for example, the increasing graph size, additional statement generation, the representation of multi-dimensional and/or nested meta-knowledge, among others. In this work, we propose an approach called RDFM to represent, store, and manage meta-knowledge. RDFM integrates attributes to the predicate to represent nested and/or multi-dimensional meta-knowledge with lesser statements generation. A query language called SPARQLM is developed to extract RDFM data. The study analyzes the performance of RDFM in terms of the number of edges, number of statements generation, data redundancy, storage, query response time, required query length, representation of meta-knowledge in nested and different dimensions. The results show that the RDFM model performs significantly and gives advantage over storage management and graph data management.

Automated summary

The proposed RDFM method in this study effectively represents, stores, and manages meta-knowledge with lesser statement generation and advantages in storage and graph data management.