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In Norway, we're working on numerous library datasets in various groups. Several of the groups have discussed data normalization; there are two camps of library database programmers in Norway; those using non-first normal form (NF2) and those who don't. The NF2 model doesn't necessarily imply normalization, whereas the other (largely relational database) models typically do. NF2 models have a perceived "usefulness built in", and the complex relations that are described are perhaps inherent in the datasets. On the other hand, complex relations might make it more difficult to query because it implies a particular (and unfamiliar?) semantics. I suspect that the extent one can query such datasets effectively is a large part of an answer to this question. The question(s): To what extent does normalization improve the usefulness of data in the semantic web? Is it better to have generic data that are more easily used, or is complexity that returns better "answers" better? |
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No reason you can't do both. A simple example; In my school RDF, we model: people have roles roles have phone numbers & email (some people may have roles with different email addresses) That's the accurate view, then we also directly state that people have phone numbers and emails (those from all their roles), this is also true and useful for less complex consumption. |
