Abstract
Resource Description Framework (RDF) can seen as a solution in today’s landscape of knowledge representation research. An RDF language has symmetrical features because subjects and objects in triples can be interchangeably used. Moreover, the regularity and symmetry of the RDF language allow knowledge representation that is easily processed by machines, and because its structure is similar to natural languages, it is reasonably readable for people. RDF provides some useful features for generalized knowledge representation. Its distributed nature, due to its identifier grounding in IRIs, naturally scales to the size of the Web. However, its use is often hidden from view and is, therefore, one of the less well-known of the knowledge representation frameworks. Therefore, we summarise RDF v1.0 and v1.1 to broaden its audience within the knowledge representation community. This article reviews current approaches, tools, and applications for mapping from relational databases to RDF and from XML to RDF. We discuss RDF serializations, including formats with support for multiple graphs and we analyze RDF compression proposals. Finally, we present a summarized formal definition of RDF 1.1 that provides additional insights into the modeling of reification, blank nodes, and entailments.
Highlights
The Resource Description Framework (RDF) version 1.1 is a modern and complete knowledge representation framework that is seemingly under-represented within the traditional knowledge representation research community
When we examine the state of the RDF data model, we see evidence of trade-offs that occurred as various constituencies took part in the design process
If rdf:langString ∈ D, for every language-tagged string E with lexical form sl and language tag tl, L I ( E) = hsl, t0l i, where t0l is tl transformed to lower case, For every other Internationalized Resource Identifiers (IRIs) d ∈ D, I (d) is the datatype identified by d, and for every literal "sl "ˆd, L I ("sl "ˆd) =
Summary
The Resource Description Framework (RDF) version 1.1 is a modern and complete knowledge representation framework that is seemingly under-represented within the traditional knowledge representation research community. Natural language can be defined as one of the methods of knowledge representation. The RDF data model was a response to this problem for knowledge representation on the World. The consistency and symmetry of the RDF language allows knowledge representation that is processed by machines, and because its structure is similar to that of natural languages, it is reasonably readable for people. Knowledge representation and data integration in the context of RDF is relevant for several reasons, including: Promotes data exchange and interoperability; facilitates the reuse of available systems and tools; enables a fair comparison of Web systems by using benchmarks. The RDF language enables large portions of existing data to be processed and analyzed This produces the need to develop the foundations of this language. We study the RDF data model, minimal and maximal representations, and show complexity bounds for the main problems
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