Abstract
The Internet serves not only as a platform for communication, transactions, and cloud storage, but also as a vast knowledge store where both people and machines can create, manipulate, infer, and utilize data and knowledge. The Semantic Web was developed to facilitate this purpose, enabling machines to understand the meaning of data and knowledge for use in decision-making. The Resource Description Framework (RDF) forms the foundation of the Semantic Web, which is organized into layers known as the Semantic Web Layer Cake. However, RDF’s basic construct is a binary relationship in the format of <subjectpredicateobject>. Representing higher-order relationships with RDF requires reification, which can be cumbersome. Time-varying data is prevalent, but cannot be adequately represented using only binary relationships. We conducted a detailed review of the literature on extending RDF with temporal data, comparing approaches for representation, querying, storage, implementation, and evaluation. In addition, we briefly reviewed approaches for extending RDF with spatial, probability, and other dimensions in conjunction with temporal data.
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