Re: [dxwg] referencing named graph of endpoint or RDF quad file (#1241)

> About your question, DCAT at the moment does not include the features you mention - the main point is whether they are in scope with DCAT or better left to DCAT profiles (as yours). We'll discuss this.

I think it is, as I'm probably not the only one dealing with RDF publication mechanisms that include quads.

> I see that you have notions as cdc:AdditionAndDeletionDistribution. Would you mind providing a summary of them?

This is a bit more difficult to explain and different from the dataset versioning as required in the #1289. I developed the notion of dataset complements, i.e. a distribution of a dataset2 that complements an older dataset1 from another or the same organization  (e.g. in the case of heavy collaboration as is normal during a construction project). As such, it's not necessary for the stakeholder who wants to add and/or remove parts of the received dataset, when creating their own contributions, to literally introduce the triples from someone else in his/her new dataset. It's enough that on DCAT level, there's the indication of which dataset distribution adds (`cdc:AdditionDistribution`), deletes (`cdc:DeletionDistribution`) or both adds and deletes (`cdc:AdditionAndDeletionDistribution`) content. For practical situations such as querying and reasoning over a dataset combined with a deletion, there's also an option to calculate the resulting dataset based on the additions and/or deletions. Using the `cdc:StandaloneDistribution` class and `cdc:standaloneOf` property, it's possible keep track of this 'standalone' distribution on dataset metadata level. Maybe the following diagram with an example might help to clearify things (where dataset C comes from stakeholder1 and dataset D comes from stakeholder2 who proposes a correction that consists of a deletion and an addition):


The benefits of this approach is that (as long as the additions and/or deletions are smaller compared to the original dataset), that it's easier to exchange changes and to keep track of who said what on data level (responsabilities). I would say that this collaboration approach is orthogonal to dataset versioning as you always complement a specific dataset version.

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Received on Thursday, 11 March 2021 17:21:29 UTC