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 »