Stefano Mazzocchi (of Freebase) and David Karger (of MIT) have been holding a slow but interesting conversation about data reconciliation. It's been phrased as a sort of debate between two arbitrarily polarized approaches to the problem of cleaning up data so that you don't have multiple variant references to the same real data-point: either you try to do this cleanup "in the data", so that it's done and you don't have to worry about it any more; or you leave the data alone and figure you'll incorporate the cleanup into your query process when you actually start asking specific questions.  

But I think this is less of a debate than Stefano and (particularly) David are making it seem. Or, rather, that the real polarization is along a slightly different axis. The biggest difference between Stefano's world and David's happens before you even start to worry about when you're going to start worrying about data cleanup: Freebase is attempting to build a single huge structured-data-representation of (eventually) all knowledge; David's work is largely about building lots of little structured-data-representations of individual (relatively) small blobs of knowledge.  

Within a dataset, though, Stefano and David (and I) are in enthusiastic agreement: internal consistency is what makes data meaningful. If your data doesn't know whether "Beatles" and "The Beatles" are one band or two different ones, your answers to any question that touches either one are liable to be so pathetically wrong that people will very quickly stop asking you things. In the classic progression from Data to Information to Knowledge to Wisdom, this is what that first step means: Data is isolated, Information is consolidated. It would be inane to be against this. (Unless, I guess, you were also against Knowledge and Wisdom.)  

It's on the definition of "within", though, that most of the interesting issues under the heading of "the Semantic Web" wobble. David says "I argued in favor of letting individuals make their own [datasets] ... and worry about merging them with other people’s data later." He really does mean individuals, and the MIT-developed tool he has in mind (Exhibit) is designed (and only suited) for fairly small datasets. Freebase is officially a database of everything, but of course within this everything are lots of subsets, and Stefano is really talking more about cleaning up these subsets than the whole universe. Reconciling all the artist references to "Beatles" and "The Beatles" from albums and songs is a straightforward practical task both beneficial and probably necessary for asking questions of a song/album/artist dataset, whether it's embedded in a larger one or not. Reconciling the "'The Beatles" who are the artist of the song "Day Tripper" with "The Beatles" who are depicted in cartoon form on a particular vintage lunchbox for sale on eBay, on the other hand, is less conceptually straightforward, and of more obscure practical import.  

The thing I'm working on at ITA falls in between Exhibit and Freebase on this dataset axis, both in capacity and design. We handle datasets much bigger than you could put in Exhibit, and allow you to explore and analyze them in ways that Exhibit cannot; but we separate datasets, partly for scalability but even more importantly to specifically keep out of any unnecessary quagmire of trying to reconcile not just the bits of data but the structures.  

And my own scouting report, from having done a lot of data-reconciliation in a system designed for it, and in other lives of a lot of more-painful data-reconciliation in various systems not really designed for it, is the same as Stefano's report from his experiences at Freebase: getting from data to information, at scale, is hard. It's mainly hard in ways that machines cannot just solve for you, and anybody who thinks RDF is a solution to data-reconciliation is, as Stefano puts it, confusing model mixability with semantic reconciliation, and has probably not noticed because they've only been playing with toy datasets, which is like thinking you're learning Architecture by building a few birdhouses.  

And this is all exactly why I have repeatedly argued for treating the "Semantic Web" technology problem as a database-tech problem first, and a web-tech problem only secondarily. David complains, justifiably, about the pain of combining two small, simple folk-dance datasets he himself cares about. But just as Stefano says in another example, the syntax problems here (e.g. text in a YouTube description box, rather than formally-labeled data records) are fairly trivial compared to the modeling differences. All the URIs and XSL transformations in the world are not going to allow every two datasets to magically operate as one. Some person, without or without tools to magnify their effort, is going to have to rephrase one dataset in the argot of the other.  

And to say another thing I've said before again, the fact that RDF doesn't itself solve these problems is not its terminal design flaw. Its terrible flaw is that it isn't even a sufficient foundation upon which to build the tools that would help solve these problems. It takes the right fundamental idea, that the most basic conceptual structure of data is things and the relationships between them, but then becomes so invertedly obsessed with the theoretical purity of this reduction that it leaves a whole layer of needed practical elaboration unbuilt. We shouldn't need abstruse design patterns to get ordered lists, or rickety reasoning engines just to get relationships that go both directions, or endless syntax officiousness that gets us expensive precision with no gain in accuracy.  

This effort has let us down. And worse, it has constrained smart people so that their earnest efforts cannot help but let us down. After years and years of waiting, we have no Semantic Web of information, we have only Linked Data, where the word "Linked" is so tenuously justified it might as well be replaced with some pink-ink-drinking Seuss pet, and the word "Data" is tragically all too accurate. We have all these computers, living abbreviated lives of quiet depreciation, filled with the data that should become our wisdom, and yearning, if they are allowed to yearn for even one thing, to be able to tell us what they know.