While syntactic interoperability is a prerequisite for effective data interchange, a whole host of other factors such as the source of the data, the target group and use cases for which it was produced have to be taken into account when re-using pieces of data. Context influences the semantics of data elements and of their content (a certain concept can have different meanings in different knowledge communities). I’d call this level of interoperability “pragmatic”, drawing on a definition of pragmatics as a subfield of linguistics: it “studies the ways in which context contributes to meaning.”
How are data and data elements actually used in context, and used differently in different contexts? Identifiers and identity conditions may vary depending on context. What about incorrect or inconsistent use of data elements? And what is/are the underlying model(s) that enable applications to share data and interpret them correctly in the given domain? If, for example, we don’t agree on the concept “book” and its properties, how can we effectively share and make sense of data about it? Is data still as valuable when torn out of its original context (Linked Data)?
I suggest that data pragmatics could look at the way people use formats, vocabularies etc. in practice as opposed to a theoretical, top-down, prescriptive view. With the number of people creating and publishing data on the web growing at an unprecedented rate, in this “open world”, we are bound to end up with data of varying quality that may or may not be interoperable on all levels.