In RDF – an Introduction I claimed that introducing any kind of continuous dimension (for example, a time dimension) is not possible, if you follow the official interpretation given in the RDF specifications. Actually it is even worse: In basic RDF even discrete dimensions cannot be modeled.

In this post I will elaborate on my claims giving a detailed description of the problem. In part 2 I will propose a new interpretation of RDF Graphs, allow for dimensions into RDF. If you are new to RDF, or terms such as *reification*, *entailment*, *fact* or *model* don’t mean much to you, you might want to read my introduction to RDF since we need these terms to talk about RDF’s incapability of modeling dimensions. I will try to present everything in a semi formal way, using some mathematical notation, but to always try to keep the post understandable for those that would not define themselves as “math people”. However, I feel that a certain amount of formality is necessary, to outline the problem and proposed solution.

**Continuous and Discrete Dimensions**

Let’s start by trying to give you an idea, of what I mean by continuous and discrete dimensions in RDF. Think of a dimension as a variable that can take values from a specified set (e.g. 1 and 2). You now define your triples (or facts) relative to the . This means, that for you have a different set of facts than for . Whether I now speak of a *continuous* or *discrete* dimension depends on the cardinality (number of elements) of the value set for . If the value set contains an infinite number of elements I speak of a continuous dimension and if the number of elements is finite I speak of a discrete dimension. Since in our example the cardinality of the value set was 2 () we have a discrete dimension. Read the rest of this entry »