RDF 1.1: Knowledge Representation and Data Integration Language for the Web
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Jan 2020.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 1; 2025: 0; 2026: 1.
3 papers.
- 2024Towards Next-Generation Urban Decision Support Systems through AI-Powered Construction of Scientific Ontology using Large Language Models -- A Case in Optimizing Intermodal Freight Transportationcited by 10
- 2026CorporateBench: Large-Scale Q&A Benchmarking with Temporal Knowledge Basescited by 1
- 2021Extracting Domain-specific Concepts from Large-scale Linked Open Datacited by 0
- 2026CorporateBench: Large-Scale Q&A Benchmarking with Temporal Knowledge Bases27 Aug 2026
- 2024Towards Next-Generation Urban Decision Support Systems through AI-Powered Construction of Scientific Ontology using Large Language Models -- A Case in Optimizing Intermodal Freight Transportation29 May 2024
- 2021Extracting Domain-specific Concepts from Large-scale Linked Open Data22 Nov 2021