The evolution of context-aware RDF knowledge graphs
Document Type
Book Chapter
Publication Title
Provenance in data science: From data models to context-aware knowledge graphs
Publisher
Springer
School
School of Science / ECU Security Research Institute
RAS ID
35851
Abstract
The many benefits of knowledge graphs using or based on the Resource Description Framework (RDF) well justify the utilization and wide deployment of a simple yet powerful, formally grounded data model, its serialization formats, vocabulary, and well-defined interpretation to be used for efficient querying, data integration, and automated reasoning. However, the simplicity of RDF comes at a price: there is no built-in mechanism for RDF statements to store metadata and context. This chapter is a critical review of different approaches proposed over the years in the Semantic Web research community to address this limitation, which are used for capturing different types of information, such as data provenance, spatiotemporal data, and certainty, which are crucial in data science applications to make statements context-aware, authoritative, verifiable, and reproducible.
DOI
10.1007/978-3-030-67681-0_1
Access Rights
subscription content
Comments
Sikos, L. F. (2020). The evolution of context-aware RDF knowledge graphs. In L. F. Sikos, O. W. Seneviratne, D. L. McGuinness (Eds.), Provenance in data science: From data models to context-aware knowledge graphs (pp. 1-10). Springer, Cham. https://link.springer.com/chapter/10.1007%2F978-3-030-67681-0_1